1102 lines
50 KiB
Python
1102 lines
50 KiB
Python
import gc
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import tracemalloc
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import os
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import logging
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from collections import OrderedDict
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from copy import copy
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from typing import Dict, Optional, Tuple, List, Union
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import modules.scripts as scripts
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from modules import shared, devices, script_callbacks, processing, masking, images
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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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import time
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from einops import rearrange
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from lib_controlnet import global_state, external_code, utils
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from lib_controlnet.utils import get_unique_axis0, align_dim_latent
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from lib_controlnet.enums import StableDiffusionVersion, HiResFixOption
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from lib_controlnet.controlnet_ui.controlnet_ui_group import ControlNetUiGroup, UiControlNetUnit
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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, StableDiffusionProcessing
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from modules.images import save_image
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from lib_controlnet.infotext import Infotext
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from modules_forge.forge_util import HWC3
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import cv2
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import numpy as np
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import torch
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from PIL import Image, ImageFilter, ImageOps
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from lib_controlnet.lvminthin import lvmin_thin, nake_nms
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# Gradio 3.32 bug fix
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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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def image_dict_from_any(image) -> Optional[Dict[str, np.ndarray]]:
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if image is None:
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return None
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if isinstance(image, (tuple, list)):
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image = {'image': image[0], 'mask': image[1]}
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elif not isinstance(image, dict):
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image = {'image': image, 'mask': None}
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else: # type(image) is dict
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# copy to enable modifying the dict and prevent response serialization error
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image = dict(image)
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if isinstance(image['image'], str):
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if os.path.exists(image['image']):
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image['image'] = np.array(Image.open(image['image'])).astype('uint8')
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elif image['image']:
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image['image'] = external_code.to_base64_nparray(image['image'])
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else:
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image['image'] = None
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# If there is no image, return image with None image and None mask
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if image['image'] is None:
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image['mask'] = None
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return image
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if 'mask' not in image or image['mask'] is None:
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image['mask'] = np.zeros_like(image['image'], dtype=np.uint8)
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elif isinstance(image['mask'], str):
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if os.path.exists(image['mask']):
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image['mask'] = np.array(Image.open(image['mask'])).astype('uint8')
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elif image['mask']:
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image['mask'] = external_code.to_base64_nparray(image['mask'])
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else:
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image['mask'] = np.zeros_like(image['image'], dtype=np.uint8)
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return image
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def prepare_mask(
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mask: Image.Image, p: processing.StableDiffusionProcessing
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) -> Image.Image:
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"""
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Prepare an image mask for the inpainting process.
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This function takes as input a PIL Image object and an instance of the
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StableDiffusionProcessing class, and performs the following steps to prepare the mask:
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1. Convert the mask to grayscale (mode "L").
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2. If the 'inpainting_mask_invert' attribute of the processing instance is True,
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invert the mask colors.
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3. If the 'mask_blur' attribute of the processing instance is greater than 0,
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apply a Gaussian blur to the mask with a radius equal to 'mask_blur'.
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Args:
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mask (Image.Image): The input mask as a PIL Image object.
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p (processing.StableDiffusionProcessing): An instance of the StableDiffusionProcessing class
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containing the processing parameters.
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Returns:
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mask (Image.Image): The prepared mask as a PIL Image object.
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"""
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mask = mask.convert("L")
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if getattr(p, "inpainting_mask_invert", False):
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mask = ImageOps.invert(mask)
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if hasattr(p, 'mask_blur_x'):
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if getattr(p, "mask_blur_x", 0) > 0:
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np_mask = np.array(mask)
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kernel_size = 2 * int(2.5 * p.mask_blur_x + 0.5) + 1
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np_mask = cv2.GaussianBlur(np_mask, (kernel_size, 1), p.mask_blur_x)
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mask = Image.fromarray(np_mask)
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if getattr(p, "mask_blur_y", 0) > 0:
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np_mask = np.array(mask)
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kernel_size = 2 * int(2.5 * p.mask_blur_y + 0.5) + 1
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np_mask = cv2.GaussianBlur(np_mask, (1, kernel_size), p.mask_blur_y)
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mask = Image.fromarray(np_mask)
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else:
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if getattr(p, "mask_blur", 0) > 0:
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mask = mask.filter(ImageFilter.GaussianBlur(p.mask_blur))
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return mask
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def set_numpy_seed(p: processing.StableDiffusionProcessing) -> Optional[int]:
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"""
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Set the random seed for NumPy based on the provided parameters.
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Args:
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p (processing.StableDiffusionProcessing): The instance of the StableDiffusionProcessing class.
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Returns:
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Optional[int]: The computed random seed if successful, or None if an exception occurs.
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This function sets the random seed for NumPy using the seed and subseed values from the given instance of
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StableDiffusionProcessing. If either seed or subseed is -1, it uses the first value from `all_seeds`.
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Otherwise, it takes the maximum of the provided seed value and 0.
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The final random seed is computed by adding the seed and subseed values, applying a bitwise AND operation
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with 0xFFFFFFFF to ensure it fits within a 32-bit integer.
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"""
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try:
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tmp_seed = int(p.all_seeds[0] if p.seed == -1 else max(int(p.seed), 0))
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tmp_subseed = int(p.all_seeds[0] if p.subseed == -1 else max(int(p.subseed), 0))
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seed = (tmp_seed + tmp_subseed) & 0xFFFFFFFF
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np.random.seed(seed)
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return seed
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except Exception as e:
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logger.warning(e)
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logger.warning('Warning: Failed to use consistent random seed.')
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return None
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def get_pytorch_control(x: np.ndarray) -> torch.Tensor:
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# A very safe method to make sure that Apple/Mac works
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y = x
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# below is very boring but do not change these. If you change these Apple or Mac may fail.
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y = torch.from_numpy(y)
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y = y.float() / 255.0
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y = rearrange(y, 'h w c -> 1 c h w')
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y = y.clone()
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y = y.to(devices.get_device_for("controlnet"))
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y = y.clone()
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return y
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class Script(scripts.Script, metaclass=(
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utils.TimeMeta if logger.level == logging.DEBUG else type)):
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model_cache = OrderedDict()
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def __init__(self) -> None:
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super().__init__()
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self.latest_network = None
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self.input_image = None
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self.latest_model_hash = ""
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self.enabled_units = []
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self.detected_map = []
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self.post_processors = []
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self.noise_modifier = None
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self.ui_batch_option_state = [external_code.BatchOption.DEFAULT.value, False]
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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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@staticmethod
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def get_default_ui_unit(is_ui=True):
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cls = UiControlNetUnit if is_ui else external_code.ControlNetUnit
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return cls(
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enabled=False,
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module="none",
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model="None"
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)
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def uigroup(self, tabname: str, is_img2img: bool, elem_id_tabname: str, photopea: Optional[Photopea]) -> Tuple[ControlNetUiGroup, gr.State]:
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group = ControlNetUiGroup(
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is_img2img,
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Script.get_default_ui_unit(),
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photopea,
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)
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return group, group.render(tabname, elem_id_tabname)
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def ui_batch_options(self, is_img2img: bool, elem_id_tabname: str):
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batch_option = gr.Radio(
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choices=[e.value for e in external_code.BatchOption],
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value=external_code.BatchOption.DEFAULT.value,
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label="Batch Option",
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elem_id=f"{elem_id_tabname}_controlnet_batch_option_radio",
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elem_classes="controlnet_batch_option_radio",
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)
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use_batch_style_align = gr.Checkbox(
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label='[StyleAlign] Align image style in the batch.'
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)
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unit_args = [batch_option, use_batch_style_align]
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def update_ui_batch_options(*args):
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self.ui_batch_option_state = args
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return
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for comp in unit_args:
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event_subscribers = []
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if hasattr(comp, "edit"):
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event_subscribers.append(comp.edit)
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elif hasattr(comp, "click"):
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event_subscribers.append(comp.click)
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elif isinstance(comp, gr.Slider) and hasattr(comp, "release"):
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event_subscribers.append(comp.release)
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elif hasattr(comp, "change"):
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event_subscribers.append(comp.change)
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if hasattr(comp, "clear"):
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event_subscribers.append(comp.clear)
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for event_subscriber in event_subscribers:
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event_subscriber(
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fn=update_ui_batch_options, inputs=unit_args
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)
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return
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def ui(self, is_img2img):
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"""this function should create gradio UI elements. See https://gradio.app/docs/#components
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The return value should be an array of all components that are used in processing.
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Values of those returned components will be passed to run() and process() functions.
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"""
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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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elem_id_tabname = ("img2img" if is_img2img else "txt2img") + "_controlnet"
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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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photopea = Photopea() if not shared.opts.data.get("controlnet_disable_photopea_edit", False) else None
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if max_models > 1:
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with gr.Tabs(elem_id=f"{elem_id_tabname}_tabs"):
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for i in range(max_models):
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with gr.Tab(f"ControlNet Unit {i}",
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elem_classes=['cnet-unit-tab']):
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group, state = self.uigroup(f"ControlNet-{i}", is_img2img, elem_id_tabname, photopea)
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ui_groups.append(group)
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controls.append(state)
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else:
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with gr.Column():
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group, state = self.uigroup(f"ControlNet", is_img2img, elem_id_tabname, photopea)
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ui_groups.append(group)
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controls.append(state)
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with gr.Accordion(f"Batch Options", open=False, elem_id="controlnet_batch_options"):
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self.ui_batch_options(is_img2img, 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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@staticmethod
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def clear_control_model_cache():
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Script.model_cache.clear()
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gc.collect()
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devices.torch_gc()
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@staticmethod
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def get_remote_call(p, attribute, default=None, idx=0, strict=False, force=False):
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if not force and not shared.opts.data.get("control_net_allow_script_control", False):
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return default
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def get_element(obj, strict=False):
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if not isinstance(obj, list):
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return obj if not strict or idx == 0 else None
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elif idx < len(obj):
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return obj[idx]
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else:
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return None
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attribute_value = get_element(getattr(p, attribute, None), strict)
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return attribute_value if attribute_value is not None else default
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@staticmethod
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def parse_remote_call(p, unit: external_code.ControlNetUnit, idx):
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selector = Script.get_remote_call
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unit.enabled = selector(p, "control_net_enabled", unit.enabled, idx, strict=True)
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unit.module = selector(p, "control_net_module", unit.module, idx)
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unit.model = selector(p, "control_net_model", unit.model, idx)
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unit.weight = selector(p, "control_net_weight", unit.weight, idx)
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unit.image = selector(p, "control_net_image", unit.image, idx)
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unit.resize_mode = selector(p, "control_net_resize_mode", unit.resize_mode, idx)
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unit.low_vram = selector(p, "control_net_lowvram", unit.low_vram, idx)
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unit.processor_res = selector(p, "control_net_pres", unit.processor_res, idx)
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unit.threshold_a = selector(p, "control_net_pthr_a", unit.threshold_a, idx)
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unit.threshold_b = selector(p, "control_net_pthr_b", unit.threshold_b, idx)
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unit.guidance_start = selector(p, "control_net_guidance_start", unit.guidance_start, idx)
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unit.guidance_end = selector(p, "control_net_guidance_end", unit.guidance_end, idx)
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# Backward compatibility. See https://github.com/Mikubill/sd-webui-controlnet/issues/1740
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# for more details.
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unit.guidance_end = selector(p, "control_net_guidance_strength", unit.guidance_end, idx)
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unit.control_mode = selector(p, "control_net_control_mode", unit.control_mode, idx)
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unit.pixel_perfect = selector(p, "control_net_pixel_perfect", unit.pixel_perfect, idx)
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return unit
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@staticmethod
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def detectmap_proc(detected_map, module, resize_mode, h, w):
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if 'inpaint' in module:
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detected_map = detected_map.astype(np.float32)
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else:
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detected_map = HWC3(detected_map)
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def safe_numpy(x):
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# A very safe method to make sure that Apple/Mac works
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y = x
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# below is very boring but do not change these. If you change these Apple or Mac may fail.
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y = y.copy()
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y = np.ascontiguousarray(y)
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y = y.copy()
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return y
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def high_quality_resize(x, size):
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# Written by lvmin
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# Super high-quality control map up-scaling, considering binary, seg, and one-pixel edges
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inpaint_mask = None
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if x.ndim == 3 and x.shape[2] == 4:
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inpaint_mask = x[:, :, 3]
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x = x[:, :, 0:3]
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if x.shape[0] != size[1] or x.shape[1] != size[0]:
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new_size_is_smaller = (size[0] * size[1]) < (x.shape[0] * x.shape[1])
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new_size_is_bigger = (size[0] * size[1]) > (x.shape[0] * x.shape[1])
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unique_color_count = len(get_unique_axis0(x.reshape(-1, x.shape[2])))
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is_one_pixel_edge = False
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is_binary = False
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if unique_color_count == 2:
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is_binary = np.min(x) < 16 and np.max(x) > 240
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if is_binary:
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xc = x
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xc = cv2.erode(xc, np.ones(shape=(3, 3), dtype=np.uint8), iterations=1)
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xc = cv2.dilate(xc, np.ones(shape=(3, 3), dtype=np.uint8), iterations=1)
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one_pixel_edge_count = np.where(xc < x)[0].shape[0]
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all_edge_count = np.where(x > 127)[0].shape[0]
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is_one_pixel_edge = one_pixel_edge_count * 2 > all_edge_count
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if 2 < unique_color_count < 200:
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interpolation = cv2.INTER_NEAREST
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elif new_size_is_smaller:
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interpolation = cv2.INTER_AREA
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else:
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interpolation = cv2.INTER_CUBIC # Must be CUBIC because we now use nms. NEVER CHANGE THIS
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y = cv2.resize(x, size, interpolation=interpolation)
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if inpaint_mask is not None:
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inpaint_mask = cv2.resize(inpaint_mask, size, interpolation=interpolation)
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if is_binary:
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y = np.mean(y.astype(np.float32), axis=2).clip(0, 255).astype(np.uint8)
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if is_one_pixel_edge:
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y = nake_nms(y)
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_, y = cv2.threshold(y, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
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y = lvmin_thin(y, prunings=new_size_is_bigger)
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else:
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_, y = cv2.threshold(y, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
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y = np.stack([y] * 3, axis=2)
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else:
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y = x
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if inpaint_mask is not None:
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inpaint_mask = (inpaint_mask > 127).astype(np.float32) * 255.0
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inpaint_mask = inpaint_mask[:, :, None].clip(0, 255).astype(np.uint8)
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y = np.concatenate([y, inpaint_mask], axis=2)
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return y
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if resize_mode == external_code.ResizeMode.RESIZE:
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detected_map = high_quality_resize(detected_map, (w, h))
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detected_map = safe_numpy(detected_map)
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return get_pytorch_control(detected_map), detected_map
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old_h, old_w, _ = detected_map.shape
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old_w = float(old_w)
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old_h = float(old_h)
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k0 = float(h) / old_h
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k1 = float(w) / old_w
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safeint = lambda x: int(np.round(x))
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if resize_mode == external_code.ResizeMode.OUTER_FIT:
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k = min(k0, k1)
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borders = np.concatenate([detected_map[0, :, :], detected_map[-1, :, :], detected_map[:, 0, :], detected_map[:, -1, :]], axis=0)
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high_quality_border_color = np.median(borders, axis=0).astype(detected_map.dtype)
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if len(high_quality_border_color) == 4:
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# Inpaint hijack
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high_quality_border_color[3] = 255
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high_quality_background = np.tile(high_quality_border_color[None, None], [h, w, 1])
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detected_map = high_quality_resize(detected_map, (safeint(old_w * k), safeint(old_h * k)))
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new_h, new_w, _ = detected_map.shape
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pad_h = max(0, (h - new_h) // 2)
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pad_w = max(0, (w - new_w) // 2)
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high_quality_background[pad_h:pad_h + new_h, pad_w:pad_w + new_w] = detected_map
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detected_map = high_quality_background
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detected_map = safe_numpy(detected_map)
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return get_pytorch_control(detected_map), detected_map
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else:
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k = max(k0, k1)
|
|
detected_map = high_quality_resize(detected_map, (safeint(old_w * k), safeint(old_h * k)))
|
|
new_h, new_w, _ = detected_map.shape
|
|
pad_h = max(0, (new_h - h) // 2)
|
|
pad_w = max(0, (new_w - w) // 2)
|
|
detected_map = detected_map[pad_h:pad_h+h, pad_w:pad_w+w]
|
|
detected_map = safe_numpy(detected_map)
|
|
return get_pytorch_control(detected_map), detected_map
|
|
|
|
@staticmethod
|
|
def get_enabled_units(p):
|
|
units = external_code.get_all_units_in_processing(p)
|
|
if len(units) == 0:
|
|
# fill a null group
|
|
remote_unit = Script.parse_remote_call(p, Script.get_default_ui_unit(), 0)
|
|
if remote_unit.enabled:
|
|
units.append(remote_unit)
|
|
|
|
enabled_units = []
|
|
for idx, unit in enumerate(units):
|
|
local_unit = Script.parse_remote_call(p, unit, idx)
|
|
if not local_unit.enabled:
|
|
continue
|
|
if hasattr(local_unit, "unfold_merged"):
|
|
enabled_units.extend(local_unit.unfold_merged())
|
|
else:
|
|
enabled_units.append(copy(local_unit))
|
|
|
|
Infotext.write_infotext(enabled_units, p)
|
|
return enabled_units
|
|
|
|
@staticmethod
|
|
def choose_input_image(
|
|
p: processing.StableDiffusionProcessing,
|
|
unit: external_code.ControlNetUnit,
|
|
idx: int
|
|
) -> Tuple[np.ndarray, external_code.ResizeMode]:
|
|
""" Choose input image from following sources with descending priority:
|
|
- p.image_control: [Deprecated] Lagacy way to pass image to controlnet.
|
|
- p.control_net_input_image: [Deprecated] Lagacy way to pass image to controlnet.
|
|
- unit.image: ControlNet tab input image.
|
|
- p.init_images: A1111 img2img tab input image.
|
|
|
|
Returns:
|
|
- The input image in ndarray form.
|
|
- The resize mode.
|
|
"""
|
|
def parse_unit_image(unit: external_code.ControlNetUnit) -> Union[List[Dict[str, np.ndarray]], Dict[str, np.ndarray]]:
|
|
unit_has_multiple_images = (
|
|
isinstance(unit.image, list) and
|
|
len(unit.image) > 0 and
|
|
"image" in unit.image[0]
|
|
)
|
|
if unit_has_multiple_images:
|
|
return [
|
|
d
|
|
for img in unit.image
|
|
for d in (image_dict_from_any(img),)
|
|
if d is not None
|
|
]
|
|
return image_dict_from_any(unit.image)
|
|
|
|
def decode_image(img) -> np.ndarray:
|
|
"""Need to check the image for API compatibility."""
|
|
if isinstance(img, str):
|
|
return np.asarray(decode_base64_to_image(image['image']))
|
|
else:
|
|
assert isinstance(img, np.ndarray)
|
|
return img
|
|
|
|
# 4 input image sources.
|
|
p_image_control = getattr(p, "image_control", None)
|
|
p_input_image = Script.get_remote_call(p, "control_net_input_image", None, idx)
|
|
image = parse_unit_image(unit)
|
|
a1111_image = getattr(p, "init_images", [None])[0]
|
|
|
|
resize_mode = external_code.resize_mode_from_value(unit.resize_mode)
|
|
|
|
if p_image_control is not None:
|
|
logger.warning("Warn: Using legacy field 'p.image_control'.")
|
|
input_image = HWC3(np.asarray(p_image_control))
|
|
elif p_input_image is not None:
|
|
logger.warning("Warn: Using legacy field 'p.controlnet_input_image'")
|
|
if isinstance(p_input_image, dict) and "mask" in p_input_image and "image" in p_input_image:
|
|
color = HWC3(np.asarray(p_input_image['image']))
|
|
alpha = np.asarray(p_input_image['mask'])[..., None]
|
|
input_image = np.concatenate([color, alpha], axis=2)
|
|
else:
|
|
input_image = HWC3(np.asarray(p_input_image))
|
|
elif image:
|
|
if isinstance(image, list):
|
|
# Add mask logic if later there is a processor that accepts mask
|
|
# on multiple inputs.
|
|
input_image = [HWC3(decode_image(img['image'])) for img in image]
|
|
else:
|
|
input_image = HWC3(decode_image(image['image']))
|
|
if 'mask' in image and image['mask'] is not None:
|
|
while len(image['mask'].shape) < 3:
|
|
image['mask'] = image['mask'][..., np.newaxis]
|
|
if 'inpaint' in unit.module:
|
|
logger.info("using inpaint as input")
|
|
color = HWC3(image['image'])
|
|
alpha = image['mask'][:, :, 0:1]
|
|
input_image = np.concatenate([color, alpha], axis=2)
|
|
elif (
|
|
not shared.opts.data.get("controlnet_ignore_noninpaint_mask", False) and
|
|
# There is wield gradio issue that would produce mask that is
|
|
# not pure color when no scribble is made on canvas.
|
|
# See https://github.com/Mikubill/sd-webui-controlnet/issues/1638.
|
|
not (
|
|
(image['mask'][:, :, 0] <= 5).all() or
|
|
(image['mask'][:, :, 0] >= 250).all()
|
|
)
|
|
):
|
|
logger.info("using mask as input")
|
|
input_image = HWC3(image['mask'][:, :, 0])
|
|
unit.module = 'none' # Always use black bg and white line
|
|
elif a1111_image is not None:
|
|
input_image = HWC3(np.asarray(a1111_image))
|
|
a1111_i2i_resize_mode = getattr(p, "resize_mode", None)
|
|
assert a1111_i2i_resize_mode is not None
|
|
resize_mode = external_code.resize_mode_from_value(a1111_i2i_resize_mode)
|
|
|
|
a1111_mask_image : Optional[Image.Image] = getattr(p, "image_mask", None)
|
|
if 'inpaint' in unit.module:
|
|
if a1111_mask_image is not None:
|
|
a1111_mask = np.array(prepare_mask(a1111_mask_image, p))
|
|
assert a1111_mask.ndim == 2
|
|
assert a1111_mask.shape[0] == input_image.shape[0]
|
|
assert a1111_mask.shape[1] == input_image.shape[1]
|
|
input_image = np.concatenate([input_image[:, :, 0:3], a1111_mask[:, :, None]], axis=2)
|
|
else:
|
|
input_image = np.concatenate([
|
|
input_image[:, :, 0:3],
|
|
np.zeros_like(input_image, dtype=np.uint8)[:, :, 0:1],
|
|
], axis=2)
|
|
else:
|
|
raise ValueError("controlnet is enabled but no input image is given")
|
|
|
|
assert isinstance(input_image, (np.ndarray, list))
|
|
return input_image, resize_mode
|
|
|
|
@staticmethod
|
|
def try_crop_image_with_a1111_mask(
|
|
p: StableDiffusionProcessing,
|
|
unit: external_code.ControlNetUnit,
|
|
input_image: np.ndarray,
|
|
resize_mode: external_code.ResizeMode,
|
|
) -> np.ndarray:
|
|
"""
|
|
Crop ControlNet input image based on A1111 inpaint mask given.
|
|
This logic is crutial in upscale scripts, as they use A1111 mask + inpaint_full_res
|
|
to crop tiles.
|
|
"""
|
|
# Note: The method determining whether the active script is an upscale script is purely
|
|
# based on `extra_generation_params` these scripts attach on `p`, and subject to change
|
|
# in the future.
|
|
# TODO: Change this to a more robust condition once A1111 offers a way to verify script name.
|
|
is_upscale_script = any("upscale" in k.lower() for k in getattr(p, "extra_generation_params", {}).keys())
|
|
logger.debug(f"is_upscale_script={is_upscale_script}")
|
|
# Note: `inpaint_full_res` is "inpaint area" on UI. The flag is `True` when "Only masked"
|
|
# option is selected.
|
|
a1111_mask_image : Optional[Image.Image] = getattr(p, "image_mask", None)
|
|
is_only_masked_inpaint = (
|
|
issubclass(type(p), StableDiffusionProcessingImg2Img) and
|
|
p.inpaint_full_res and
|
|
a1111_mask_image is not None
|
|
)
|
|
if (
|
|
'reference' not in unit.module
|
|
and is_only_masked_inpaint
|
|
and (is_upscale_script or unit.inpaint_crop_input_image)
|
|
):
|
|
logger.debug("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
|
|
|
|
@staticmethod
|
|
def bound_check_params(unit: external_code.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 (external_code.ControlNetUnit): The ControlNetUnit instance to check.
|
|
"""
|
|
cfg = preprocessor_sliders_config.get(
|
|
global_state.get_module_basename(unit.module), [])
|
|
defaults = {
|
|
param: cfg_default['value']
|
|
for param, cfg_default in zip(
|
|
("processor_res", 'threshold_a', 'threshold_b'), cfg)
|
|
if cfg_default is not None
|
|
}
|
|
for param, default_value in defaults.items():
|
|
value = getattr(unit, param)
|
|
if value < 0:
|
|
setattr(unit, param, default_value)
|
|
logger.warning(f'[{unit.module}.{param}] Invalid value({value}), using default value {default_value}.')
|
|
|
|
@staticmethod
|
|
def check_sd_version_compatible(unit: external_code.ControlNetUnit) -> None:
|
|
"""
|
|
Checks whether the given ControlNet unit has model compatible with the currently
|
|
active sd model. An exception is thrown if ControlNet unit is detected to be
|
|
incompatible.
|
|
"""
|
|
sd_version = global_state.get_sd_version()
|
|
assert sd_version != StableDiffusionVersion.UNKNOWN
|
|
|
|
if "revision" in unit.module.lower() and sd_version != StableDiffusionVersion.SDXL:
|
|
raise Exception(f"Preprocessor 'revision' only supports SDXL. Current SD base model is {sd_version}.")
|
|
|
|
# No need to check if the ControlModelType does not require model to be present.
|
|
if unit.model is None or unit.model.lower() == "none":
|
|
return
|
|
|
|
cnet_sd_version = StableDiffusionVersion.detect_from_model_name(unit.model)
|
|
|
|
if cnet_sd_version == StableDiffusionVersion.UNKNOWN:
|
|
logger.warn(f"Unable to determine version for ControlNet model '{unit.model}'.")
|
|
return
|
|
|
|
if not sd_version.is_compatible_with(cnet_sd_version):
|
|
raise Exception(f"ControlNet model {unit.model}({cnet_sd_version}) is not compatible with sd model({sd_version})")
|
|
|
|
@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 = (
|
|
isinstance(p, StableDiffusionProcessingTxt2Img)
|
|
and getattr(p, 'enable_hr', False)
|
|
)
|
|
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
|
|
|
|
def controlnet_main_entry(self, p):
|
|
sd_ldm = p.sd_model
|
|
unet = sd_ldm.model.diffusion_model
|
|
self.noise_modifier = None
|
|
|
|
setattr(p, 'controlnet_control_loras', [])
|
|
|
|
if self.latest_network is not None:
|
|
# always restore (~0.05s)
|
|
self.latest_network.restore()
|
|
|
|
# always clear (~0.05s)
|
|
clear_all_secondary_control_models(unet)
|
|
|
|
if not batch_hijack.instance.is_batch:
|
|
self.enabled_units = Script.get_enabled_units(p)
|
|
|
|
batch_option_uint_separate = self.ui_batch_option_state[0] == external_code.BatchOption.SEPARATE.value
|
|
batch_option_style_align = self.ui_batch_option_state[1]
|
|
|
|
if len(self.enabled_units) == 0 and not batch_option_style_align:
|
|
self.latest_network = None
|
|
return
|
|
|
|
logger.info(f"unit_separate = {batch_option_uint_separate}, style_align = {batch_option_style_align}")
|
|
|
|
detected_maps = []
|
|
forward_params = []
|
|
post_processors = []
|
|
|
|
# cache stuff
|
|
if self.latest_model_hash != p.sd_model.sd_model_hash:
|
|
Script.clear_control_model_cache()
|
|
|
|
for idx, unit in enumerate(self.enabled_units):
|
|
unit.module = global_state.get_module_basename(unit.module)
|
|
|
|
# unload unused preproc
|
|
module_list = [unit.module for unit in self.enabled_units]
|
|
for key in self.unloadable:
|
|
if key not in module_list:
|
|
self.unloadable.get(key, lambda:None)()
|
|
|
|
self.latest_model_hash = p.sd_model.sd_model_hash
|
|
high_res_fix = isinstance(p, StableDiffusionProcessingTxt2Img) and getattr(p, 'enable_hr', False)
|
|
h, w, hr_y, hr_x = Script.get_target_dimensions(p)
|
|
|
|
for idx, unit in enumerate(self.enabled_units):
|
|
Script.bound_check_params(unit)
|
|
Script.check_sd_version_compatible(unit)
|
|
if (
|
|
"ip-adapter" in unit.module and
|
|
not global_state.ip_adapter_pairing_model[unit.module](unit.model)
|
|
):
|
|
logger.error(f"Invalid pair of IP-Adapter preprocessor({unit.module}) and model({unit.model}).\n"
|
|
"Please follow following pairing logic:\n"
|
|
+ global_state.ip_adapter_pairing_logic_text)
|
|
continue
|
|
|
|
if (
|
|
'inpaint_only' == unit.module and
|
|
issubclass(type(p), StableDiffusionProcessingImg2Img) and
|
|
p.image_mask is not None
|
|
):
|
|
logger.warning('A1111 inpaint and ControlNet inpaint duplicated. Falls back to inpaint_global_harmonious.')
|
|
unit.module = 'inpaint'
|
|
|
|
if unit.module in model_free_preprocessors:
|
|
model_net = None
|
|
if 'reference' in unit.module:
|
|
control_model_type = ControlModelType.AttentionInjection
|
|
elif 'revision' in unit.module:
|
|
control_model_type = ControlModelType.ReVision
|
|
else:
|
|
raise Exception("Unable to determine control_model_type.")
|
|
else:
|
|
model_net, control_model_type = Script.load_control_model(p, unet, unit.model)
|
|
model_net.reset()
|
|
|
|
if control_model_type == ControlModelType.ControlLoRA:
|
|
control_lora = model_net.control_model
|
|
bind_control_lora(unet, control_lora)
|
|
p.controlnet_control_loras.append(control_lora)
|
|
|
|
input_image, resize_mode = Script.choose_input_image(p, unit, idx)
|
|
if isinstance(input_image, list):
|
|
assert unit.accepts_multiple_inputs()
|
|
input_images = input_image
|
|
else: # Following operations are only for single input image.
|
|
input_image = Script.try_crop_image_with_a1111_mask(p, unit, input_image, resize_mode)
|
|
input_image = np.ascontiguousarray(input_image.copy()).copy() # safe numpy
|
|
if unit.module == 'inpaint_only+lama' and resize_mode == external_code.ResizeMode.OUTER_FIT:
|
|
# inpaint_only+lama is special and required outpaint fix
|
|
_, input_image = Script.detectmap_proc(input_image, unit.module, resize_mode, hr_y, hr_x)
|
|
if unit.pixel_perfect:
|
|
unit.processor_res = external_code.pixel_perfect_resolution(
|
|
input_image,
|
|
target_H=h,
|
|
target_W=w,
|
|
resize_mode=resize_mode,
|
|
)
|
|
input_images = [input_image]
|
|
# Preprocessor result may depend on numpy random operations, use the
|
|
# random seed in `StableDiffusionProcessing` to make the
|
|
# preprocessor result reproducable.
|
|
# Currently following preprocessors use numpy random:
|
|
# - shuffle
|
|
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}')
|
|
|
|
def store_detected_map(detected_map, module: str) -> None:
|
|
if unit.save_detected_map:
|
|
detected_maps.append((detected_map, module))
|
|
|
|
def preprocess_input_image(input_image: np.ndarray):
|
|
""" Preprocess single input image. """
|
|
detected_map, is_image = self.preprocessor[unit.module](
|
|
input_image,
|
|
res=unit.processor_res,
|
|
thr_a=unit.threshold_a,
|
|
thr_b=unit.threshold_b,
|
|
low_vram=(
|
|
("clip" in unit.module or unit.module == "ip-adapter_face_id_plus") and
|
|
shared.opts.data.get("controlnet_clip_detector_on_cpu", False)
|
|
),
|
|
)
|
|
if high_res_fix:
|
|
if is_image:
|
|
hr_control, hr_detected_map = Script.detectmap_proc(detected_map, unit.module, resize_mode, hr_y, hr_x)
|
|
store_detected_map(hr_detected_map, unit.module)
|
|
else:
|
|
hr_control = detected_map
|
|
else:
|
|
hr_control = None
|
|
|
|
if is_image:
|
|
control, detected_map = Script.detectmap_proc(detected_map, unit.module, resize_mode, h, w)
|
|
store_detected_map(detected_map, unit.module)
|
|
else:
|
|
control = detected_map
|
|
store_detected_map(input_image, unit.module)
|
|
|
|
if control_model_type == ControlModelType.T2I_StyleAdapter:
|
|
control = control['last_hidden_state']
|
|
|
|
if control_model_type == ControlModelType.ReVision:
|
|
control = control['image_embeds']
|
|
return control, hr_control
|
|
|
|
controls, hr_controls = list(zip(*[preprocess_input_image(img) for img in input_images]))
|
|
if len(controls) == len(hr_controls) == 1:
|
|
control = controls[0]
|
|
hr_control = hr_controls[0]
|
|
else:
|
|
control = controls
|
|
hr_control = hr_controls
|
|
|
|
preprocessor_dict = dict(
|
|
name=unit.module,
|
|
preprocessor_resolution=unit.processor_res,
|
|
threshold_a=unit.threshold_a,
|
|
threshold_b=unit.threshold_b
|
|
)
|
|
|
|
global_average_pooling = (
|
|
control_model_type.is_controlnet() and
|
|
model_net.control_model.global_average_pooling
|
|
)
|
|
control_mode = external_code.control_mode_from_value(unit.control_mode)
|
|
forward_param = ControlParams(
|
|
control_model=model_net,
|
|
preprocessor=preprocessor_dict,
|
|
hint_cond=control,
|
|
weight=unit.weight,
|
|
guidance_stopped=False,
|
|
start_guidance_percent=unit.guidance_start,
|
|
stop_guidance_percent=unit.guidance_end,
|
|
advanced_weighting=unit.advanced_weighting,
|
|
control_model_type=control_model_type,
|
|
global_average_pooling=global_average_pooling,
|
|
hr_hint_cond=hr_control,
|
|
hr_option=HiResFixOption.from_value(unit.hr_option) if high_res_fix else HiResFixOption.BOTH,
|
|
soft_injection=control_mode != external_code.ControlMode.BALANCED,
|
|
cfg_injection=control_mode == external_code.ControlMode.CONTROL,
|
|
)
|
|
forward_params.append(forward_param)
|
|
|
|
if 'inpaint_only' in unit.module:
|
|
final_inpaint_feed = hr_control if hr_control is not None else control
|
|
final_inpaint_feed = final_inpaint_feed.detach().cpu().numpy()
|
|
final_inpaint_feed = np.ascontiguousarray(final_inpaint_feed).copy()
|
|
final_inpaint_mask = final_inpaint_feed[0, 3, :, :].astype(np.float32)
|
|
final_inpaint_raw = final_inpaint_feed[0, :3].astype(np.float32)
|
|
sigma = shared.opts.data.get("control_net_inpaint_blur_sigma", 7)
|
|
final_inpaint_mask = cv2.dilate(final_inpaint_mask, np.ones((sigma, sigma), dtype=np.uint8))
|
|
final_inpaint_mask = cv2.blur(final_inpaint_mask, (sigma, sigma))[None]
|
|
_, Hmask, Wmask = final_inpaint_mask.shape
|
|
final_inpaint_raw = torch.from_numpy(np.ascontiguousarray(final_inpaint_raw).copy())
|
|
final_inpaint_mask = torch.from_numpy(np.ascontiguousarray(final_inpaint_mask).copy())
|
|
|
|
def inpaint_only_post_processing(x):
|
|
_, H, W = x.shape
|
|
if Hmask != H or Wmask != W:
|
|
logger.error('Error: ControlNet find post-processing resolution mismatch. This could be related to other extensions hacked processing.')
|
|
return x
|
|
r = final_inpaint_raw.to(x.dtype).to(x.device)
|
|
m = final_inpaint_mask.to(x.dtype).to(x.device)
|
|
y = m * x.clip(0, 1) + (1 - m) * r
|
|
y = y.clip(0, 1)
|
|
return y
|
|
|
|
post_processors.append(inpaint_only_post_processing)
|
|
|
|
if 'recolor' in unit.module:
|
|
final_feed = hr_control if hr_control is not None else control
|
|
final_feed = final_feed.detach().cpu().numpy()
|
|
final_feed = np.ascontiguousarray(final_feed).copy()
|
|
final_feed = final_feed[0, 0, :, :].astype(np.float32)
|
|
final_feed = (final_feed * 255).clip(0, 255).astype(np.uint8)
|
|
Hfeed, Wfeed = final_feed.shape
|
|
|
|
if 'luminance' in unit.module:
|
|
|
|
def recolor_luminance_post_processing(x):
|
|
C, H, W = x.shape
|
|
if Hfeed != H or Wfeed != W or C != 3:
|
|
logger.error('Error: ControlNet find post-processing resolution mismatch. This could be related to other extensions hacked processing.')
|
|
return x
|
|
h = x.detach().cpu().numpy().transpose((1, 2, 0))
|
|
h = (h * 255).clip(0, 255).astype(np.uint8)
|
|
h = cv2.cvtColor(h, cv2.COLOR_RGB2LAB)
|
|
h[:, :, 0] = final_feed
|
|
h = cv2.cvtColor(h, cv2.COLOR_LAB2RGB)
|
|
h = (h.astype(np.float32) / 255.0).transpose((2, 0, 1))
|
|
y = torch.from_numpy(h).clip(0, 1).to(x)
|
|
return y
|
|
|
|
post_processors.append(recolor_luminance_post_processing)
|
|
|
|
if 'intensity' in unit.module:
|
|
|
|
def recolor_intensity_post_processing(x):
|
|
C, H, W = x.shape
|
|
if Hfeed != H or Wfeed != W or C != 3:
|
|
logger.error('Error: ControlNet find post-processing resolution mismatch. This could be related to other extensions hacked processing.')
|
|
return x
|
|
h = x.detach().cpu().numpy().transpose((1, 2, 0))
|
|
h = (h * 255).clip(0, 255).astype(np.uint8)
|
|
h = cv2.cvtColor(h, cv2.COLOR_RGB2HSV)
|
|
h[:, :, 2] = final_feed
|
|
h = cv2.cvtColor(h, cv2.COLOR_HSV2RGB)
|
|
h = (h.astype(np.float32) / 255.0).transpose((2, 0, 1))
|
|
y = torch.from_numpy(h).clip(0, 1).to(x)
|
|
return y
|
|
|
|
post_processors.append(recolor_intensity_post_processing)
|
|
|
|
if '+lama' in unit.module:
|
|
forward_param.used_hint_cond_latent = hook.UnetHook.call_vae_using_process(p, control)
|
|
self.noise_modifier = forward_param.used_hint_cond_latent
|
|
|
|
del model_net
|
|
|
|
is_low_vram = any(unit.low_vram for unit in self.enabled_units)
|
|
|
|
for i, param in enumerate(forward_params):
|
|
if param.control_model_type == ControlModelType.IPAdapter:
|
|
param.control_model.hook(
|
|
model=unet,
|
|
preprocessor_outputs=param.hint_cond,
|
|
weight=param.weight,
|
|
dtype=torch.float32,
|
|
start=param.start_guidance_percent,
|
|
end=param.stop_guidance_percent
|
|
)
|
|
if param.control_model_type == ControlModelType.Controlllite:
|
|
param.control_model.hook(
|
|
model=unet,
|
|
cond=param.hint_cond,
|
|
weight=param.weight,
|
|
start=param.start_guidance_percent,
|
|
end=param.stop_guidance_percent
|
|
)
|
|
if param.control_model_type == ControlModelType.InstantID:
|
|
# For instant_id we always expect ip-adapter model followed
|
|
# by ControlNet model.
|
|
assert i > 0, "InstantID control model should follow ipadapter model."
|
|
ip_adapter_param = forward_params[i - 1]
|
|
assert ip_adapter_param.control_model_type == ControlModelType.IPAdapter, \
|
|
"InstantID control model should follow ipadapter model."
|
|
control_model = ip_adapter_param.control_model
|
|
assert hasattr(control_model, "image_emb")
|
|
param.control_context_override = control_model.image_emb
|
|
|
|
self.latest_network = UnetHook(lowvram=is_low_vram)
|
|
self.latest_network.hook(model=unet, sd_ldm=sd_ldm, control_params=forward_params, process=p,
|
|
batch_option_uint_separate=batch_option_uint_separate,
|
|
batch_option_style_align=batch_option_style_align)
|
|
|
|
self.detected_map = detected_maps
|
|
self.post_processors = post_processors
|
|
|
|
def process_unit_after_click_generate(self, p, unit, *args, **kwargs):
|
|
return
|
|
|
|
def process_unit_before_every_sampling(self, p, unit, *args, **kwargs):
|
|
return
|
|
|
|
def process(self, p, *args, **kwargs):
|
|
for unit in Script.get_enabled_units(p):
|
|
self.process_unit_after_click_generate(p, unit, *args, **kwargs)
|
|
return
|
|
|
|
def process_before_every_sampling(self, p, *args, **kwargs):
|
|
for unit in Script.get_enabled_units(p):
|
|
self.process_unit_before_every_sampling(p, unit, *args, **kwargs)
|
|
return
|
|
|
|
def postprocess(self, p, processed, *args):
|
|
return
|
|
|
|
def batch_tab_process(self, p, batches, *args, **kwargs):
|
|
self.enabled_units = Script.get_enabled_units(p)
|
|
for unit_i, unit in enumerate(self.enabled_units):
|
|
unit.batch_images = iter([batch[unit_i] for batch in batches])
|
|
|
|
def batch_tab_process_each(self, p, *args, **kwargs):
|
|
for unit_i, unit in enumerate(self.enabled_units):
|
|
if getattr(unit, 'loopback', False):
|
|
continue
|
|
|
|
unit.image = next(unit.batch_images)
|
|
|
|
def batch_tab_postprocess_each(self, p, processed, *args, **kwargs):
|
|
for unit_i, unit in enumerate(self.enabled_units):
|
|
if getattr(unit, 'loopback', False):
|
|
output_images = getattr(processed, 'images', [])[processed.index_of_first_image:]
|
|
if output_images:
|
|
unit.image = np.array(output_images[0])
|
|
else:
|
|
logger.warning(f'Warning: No loopback image found for controlnet unit {unit_i}. '
|
|
f'Using control map from last batch iteration instead')
|
|
|
|
def batch_tab_postprocess(self, p, *args, **kwargs):
|
|
self.enabled_units.clear()
|
|
self.input_image = None
|
|
if self.latest_network is None: return
|
|
|
|
self.latest_network.restore()
|
|
self.latest_network = None
|
|
self.detected_map.clear()
|
|
|
|
|
|
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(
|
|
2, "Model cache size (requires restart)", gr.Slider, {"minimum": 1, "maximum": 10, "step": 1}, section=section))
|
|
shared.opts.add_option("control_net_inpaint_blur_sigma", shared.OptionInfo(
|
|
7, "ControlNet inpainting Gaussian blur sigma", gr.Slider, {"minimum": 0, "maximum": 64, "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_ignore_noninpaint_mask", shared.OptionInfo(
|
|
False, "Ignore mask on ControlNet input image if control type is not inpaint",
|
|
gr.Checkbox, {"interactive": True}, section=section))
|
|
shared.opts.add_option("controlnet_clip_detector_on_cpu", shared.OptionInfo(
|
|
False, "Load CLIP preprocessor model on CPU",
|
|
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) |