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	task_stablepy = {
	'txt2img': 'txt2img',
	'img2img': 'img2img',
	'inpaint': 'inpaint',
	'sdxl_canny T2I Adapter': 'sdxl_canny',
	'sdxl_sketch T2I Adapter': 'sdxl_sketch',
	'sdxl_lineart T2I Adapter': 'sdxl_lineart',
	'sdxl_depth-midas T2I Adapter': 'sdxl_depth-midas',
	'sdxl_openpose T2I Adapter': 'sdxl_openpose',
	'sd_openpose ControlNet': 'openpose',
	'sd_canny ControlNet': 'canny',
	'sd_mlsd ControlNet': 'mlsd',
	'sd_scribble ControlNet': 'scribble',
	'sd_softedge ControlNet': 'softedge',
	'sd_segmentation ControlNet': 'segmentation',
	'sd_depth ControlNet': 'depth',
	'sd_normalbae ControlNet': 'normalbae',
	'sd_lineart ControlNet': 'lineart',
	'sd_lineart_anime ControlNet': 'lineart_anime',
	'sd_shuffle ControlNet': 'shuffle',
	'sd_ip2p ControlNet': 'ip2p',
	}

	task_model_list = list(task_stablepy.keys())

	#######################
	# UTILS
	#######################
	import spaces
	import os
	from stablepy import Model_Diffusers
	from stablepy.diffusers_vanilla.model import scheduler_names
	from stablepy.diffusers_vanilla.style_prompt_config import STYLE_NAMES
	import torch
	import re
	import shutil


	preprocessor_controlnet = {
	"openpose": [
	"Openpose",
	"None",
	],
	"scribble": [
	"HED",
	"Pidinet",
	"None",
	],
	"softedge": [
	"Pidinet",
	"HED",
	"HED safe",
	"Pidinet safe",
	"None",
	],
	"segmentation": [
	"UPerNet",
	"None",
	],
	"depth": [
	"DPT",
	"Midas",
	"None",
	],
	"normalbae": [
	"NormalBae",
	"None",
	],
	"lineart": [
	"Lineart",
	"Lineart coarse",
	"LineartAnime",
	"None",
	"None (anime)",
	],
	"shuffle": [
	"ContentShuffle",
	"None",
	],
	"canny": [
	"Canny"
	],
	"mlsd": [
	"MLSD"
	],
	"ip2p": [
	"ip2p"
	]
	}


	def download_things(directory, url, hf_token="", civitai_api_key=""):
	url = url.strip()

	if "drive.google.com" in url:
	original_dir = os.getcwd()
	os.chdir(directory)
	os.system(f"gdown --fuzzy {url}")
	os.chdir(original_dir)
	elif "huggingface.co" in url:
	url = url.replace("?download=true", "")
	if "/blob/" in url:
	url = url.replace("/blob/", "/resolve/")
	user_header = f'"Authorization: Bearer {hf_token}"'
	if hf_token:
	os.system(f"aria2c --console-log-level=error --summary-interval=10 --header={user_header} -c -x 16 -k 1M -s 16 {url} -d {directory} -o {url.split('/')[-1]}")
	else:
	os.system (f"aria2c --optimize-concurrent-downloads --console-log-level=error --summary-interval=10 -c -x 16 -k 1M -s 16 {url} -d {directory} -o {url.split('/')[-1]}")
	elif "civitai.com" in url:
	if "?" in url:
	url = url.split("?")[0]
	if civitai_api_key:
	url = url + f"?token={civitai_api_key}"
	os.system(f"aria2c --console-log-level=error --summary-interval=10 -c -x 16 -k 1M -s 16 -d {directory} {url}")
	else:
	print("\033[91mYou need an API key to download Civitai models.\033[0m")
	else:
	os.system(f"aria2c --console-log-level=error --summary-interval=10 -c -x 16 -k 1M -s 16 -d {directory} {url}")


	def get_model_list(directory_path):
	model_list = []
	valid_extensions = {'.ckpt' , '.pt', '.pth', '.safetensors', '.bin'}

	for filename in os.listdir(directory_path):
	if os.path.splitext(filename)[1] in valid_extensions:
	name_without_extension = os.path.splitext(filename)[0]
	file_path = os.path.join(directory_path, filename)
	# model_list.append((name_without_extension, file_path))
	model_list.append(file_path)
	print('\033[34mFILE: ' + file_path + '\033[0m')
	return model_list


	def process_string(input_string):
	parts = input_string.split('/')

	if len(parts) == 2:
	first_element = parts[1]
	complete_string = input_string
	result = (first_element, complete_string)
	return result
	else:
	return None


	directory_models = 'models'
	os.makedirs(directory_models, exist_ok=True)
	directory_loras = 'loras'
	os.makedirs(directory_loras, exist_ok=True)
	directory_vaes = 'vaes'
	os.makedirs(directory_vaes, exist_ok=True)

	# - Download SD 1.5 Models
	download_model = "https://huggingface.co/frankjoshua/toonyou_beta6/resolve/main/toonyou_beta6.safetensors"
	# - Download VAEs
	download_vae = "https://huggingface.co/madebyollin/sdxl-vae-fp16-fix/resolve/main/sdxl.vae.safetensors?download=true, https://huggingface.co/nubby/blessed-sdxl-vae-fp16-fix/resolve/main/sdxl_vae-fp16fix-c-1.1-b-0.5.safetensors?download=true, https://huggingface.co/nubby/blessed-sdxl-vae-fp16-fix/resolve/main/sdxl_vae-fp16fix-blessed.safetensors?download=true, https://huggingface.co/digiplay/VAE/resolve/main/vividReal_v20.safetensors?download=true, https://huggingface.co/fp16-guy/anything_kl-f8-anime2_vae-ft-mse-840000-ema-pruned_blessed_clearvae_fp16_cleaned/resolve/main/kl-f8-anime2_fp16.safetensors?download=true, https://huggingface.co/fp16-guy/anything_kl-f8-anime2_vae-ft-mse-840000-ema-pruned_blessed_clearvae_fp16_cleaned/resolve/main/ClearVAE_V2.3_fp16.safetensors?download=true, https://huggingface.co/fp16-guy/anything_kl-f8-anime2_vae-ft-mse-840000-ema-pruned_blessed_clearvae_fp16_cleaned/resolve/main/vae-ft-mse-840000-ema-pruned_fp16.safetensors?download=true, https://huggingface.co/fp16-guy/anything_kl-f8-anime2_vae-ft-mse-840000-ema-pruned_blessed_clearvae_fp16_cleaned/resolve/main/blessed2_fp16.safetensors?download=true"
	# - Download LoRAs
	download_lora = "https://civitai.com/api/download/models/135867, https://civitai.com/api/download/models/135931, https://civitai.com/api/download/models/177492, https://civitai.com/api/download/models/145907, https://huggingface.co/Linaqruf/anime-detailer-xl-lora/resolve/main/anime-detailer-xl.safetensors?download=true, https://huggingface.co/Linaqruf/style-enhancer-xl-lora/resolve/main/style-enhancer-xl.safetensors?download=true, https://civitai.com/api/download/models/28609"
	load_diffusers_format_model = [
	'stabilityai/stable-diffusion-xl-base-1.0',
	'misri/epicrealismXL_v7FinalDestination',
	'misri/juggernautXL_juggernautX',
	'misri/anima_pencil-XL-v4.0.0',
	'cagliostrolab/animagine-xl-3.1',
	'misri/kohakuXLEpsilon_rev1',
	'kitty7779/ponyDiffusionV6XL',
	'runwayml/stable-diffusion-v1-5',
	'digiplay/majicMIX_realistic_v6',
	'digiplay/majicMIX_realistic_v7',
	'digiplay/DreamShaper_8',
	'digiplay/BeautifulArt_v1',
	'digiplay/DarkSushi2.5D_v1',
	]

	CIVITAI_API_KEY = os.environ.get("CIVITAI_API_KEY")
	hf_token = os.environ.get("HF_TOKEN")

	# Download stuffs
	for url in [url.strip() for url in download_model.split(',')]:
	if not os.path.exists(f"./models/{url.split('/')[-1]}"):
	download_things(directory_models, url, hf_token, CIVITAI_API_KEY)
	for url in [url.strip() for url in download_vae.split(',')]:
	if not os.path.exists(f"./vaes/{url.split('/')[-1]}"):
	download_things(directory_vaes, url, hf_token, CIVITAI_API_KEY)
	for url in [url.strip() for url in download_lora.split(',')]:
	if not os.path.exists(f"./loras/{url.split('/')[-1]}"):
	download_things(directory_loras, url, hf_token, CIVITAI_API_KEY)

	# Download Embeddings
	directory_embeds = 'embedings'
	os.makedirs(directory_embeds, exist_ok=True)
	download_embeds = [
	'https://huggingface.co/datasets/Nerfgun3/bad_prompt/resolve/main/bad_prompt.pt',
	'https://huggingface.co/datasets/Nerfgun3/bad_prompt/blob/main/bad_prompt_version2.pt',
	'https://huggingface.co/embed/EasyNegative/resolve/main/EasyNegative.safetensors',
	'https://huggingface.co/embed/negative/resolve/main/EasyNegativeV2.safetensors',
	'https://huggingface.co/embed/negative/resolve/main/bad-hands-5.pt',
	'https://huggingface.co/embed/negative/resolve/main/bad-artist.pt',
	'https://huggingface.co/embed/negative/resolve/main/ng_deepnegative_v1_75t.pt',
	'https://huggingface.co/embed/negative/resolve/main/bad-artist-anime.pt',
	'https://huggingface.co/embed/negative/resolve/main/bad-image-v2-39000.pt',
	'https://huggingface.co/embed/negative/resolve/main/verybadimagenegative_v1.3.pt',
	]

	for url_embed in download_embeds:
	if not os.path.exists(f"./embedings/{url_embed.split('/')[-1]}"):
	download_things(directory_embeds, url_embed, hf_token, CIVITAI_API_KEY)

	# Build list models
	embed_list = get_model_list(directory_embeds)
	model_list = get_model_list(directory_models)
	model_list = load_diffusers_format_model + model_list
	lora_model_list = get_model_list(directory_loras)
	lora_model_list.insert(0, "None")
	vae_model_list = get_model_list(directory_vaes)
	vae_model_list.insert(0, "None")

	print('\033[33m🏁 Download and listing of valid models completed.\033[0m')

	upscaler_dict_gui = {
	None : None,
	"Lanczos" : "Lanczos",
	"Nearest" : "Nearest",
	"RealESRGAN_x4plus" : "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth",
	"RealESRNet_x4plus" : "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/RealESRNet_x4plus.pth",
	"RealESRGAN_x4plus_anime_6B": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth",
	"RealESRGAN_x2plus": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth",
	"realesr-animevideov3": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-animevideov3.pth",
	"realesr-general-x4v3": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth",
	"realesr-general-wdn-x4v3" : "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-wdn-x4v3.pth",
	"4x-UltraSharp" : "https://huggingface.co/Shandypur/ESRGAN-4x-UltraSharp/resolve/main/4x-UltraSharp.pth",
	"4x_foolhardy_Remacri" : "https://huggingface.co/FacehugmanIII/4x_foolhardy_Remacri/resolve/main/4x_foolhardy_Remacri.pth",
	"Remacri4xExtraSmoother" : "https://huggingface.co/hollowstrawberry/upscalers-backup/resolve/main/ESRGAN/Remacri%204x%20ExtraSmoother.pth",
	"AnimeSharp4x" : "https://huggingface.co/hollowstrawberry/upscalers-backup/resolve/main/ESRGAN/AnimeSharp%204x.pth",
	"lollypop" : "https://huggingface.co/hollowstrawberry/upscalers-backup/resolve/main/ESRGAN/lollypop.pth",
	"RealisticRescaler4x" : "https://huggingface.co/hollowstrawberry/upscalers-backup/resolve/main/ESRGAN/RealisticRescaler%204x.pth",
	"NickelbackFS4x" : "https://huggingface.co/hollowstrawberry/upscalers-backup/resolve/main/ESRGAN/NickelbackFS%204x.pth"
	}


	def extract_parameters(input_string):
	parameters = {}
	input_string = input_string.replace("\n", "")

	if not "Negative prompt:" in input_string:
	print("Negative prompt not detected")
	parameters["prompt"] = input_string
	return parameters

	parm = input_string.split("Negative prompt:")
	parameters["prompt"] = parm[0]
	if not "Steps:" in parm[1]:
	print("Steps not detected")
	parameters["neg_prompt"] = parm[1]
	return parameters
	parm = parm[1].split("Steps:")
	parameters["neg_prompt"] = parm[0]
	input_string = "Steps:" + parm[1]

	# Extracting Steps
	steps_match = re.search(r'Steps: (\d+)', input_string)
	if steps_match:
	parameters['Steps'] = int(steps_match.group(1))

	# Extracting Size
	size_match = re.search(r'Size: (\d+x\d+)', input_string)
	if size_match:
	parameters['Size'] = size_match.group(1)
	width, height = map(int, parameters['Size'].split('x'))
	parameters['width'] = width
	parameters['height'] = height

	# Extracting other parameters
	other_parameters = re.findall(r'(\w+): (.*?)(?=, \w+\|$)', input_string)
	for param in other_parameters:
	parameters[param[0]] = param[1].strip('"')

	return parameters


	#######################
	# GUI
	#######################
	import spaces
	import gradio as gr
	from PIL import Image
	import IPython.display
	import time, json
	from IPython.utils import capture
	import logging
	logging.getLogger("diffusers").setLevel(logging.ERROR)
	import diffusers
	diffusers.utils.logging.set_verbosity(40)
	import warnings
	warnings.filterwarnings(action="ignore", category=FutureWarning, module="diffusers")
	warnings.filterwarnings(action="ignore", category=UserWarning, module="diffusers")
	warnings.filterwarnings(action="ignore", category=FutureWarning, module="transformers")
	from stablepy import logger
	logger.setLevel(logging.DEBUG)


	class GuiSD:
	def __init__(self):
	self.model = None

	@spaces.GPU
	def infer_short(self, model, pipe_params):
	images, image_list = model(**pipe_params)
	return images

	@spaces.GPU(duration=120)
	def infer(self, model, pipe_params):
	images, image_list = model(**pipe_params)
	return images

	def generate_pipeline(
	self,
	prompt,
	neg_prompt,
	num_images,
	steps,
	cfg,
	clip_skip,
	seed,
	lora1,
	lora_scale1,
	lora2,
	lora_scale2,
	lora3,
	lora_scale3,
	lora4,
	lora_scale4,
	lora5,
	lora_scale5,
	sampler,
	img_height,
	img_width,
	model_name,
	vae_model,
	task,
	image_control,
	preprocessor_name,
	preprocess_resolution,
	image_resolution,
	style_prompt, # list []
	style_json_file,
	image_mask,
	strength,
	low_threshold,
	high_threshold,
	value_threshold,
	distance_threshold,
	controlnet_output_scaling_in_unet,
	controlnet_start_threshold,
	controlnet_stop_threshold,
	textual_inversion,
	syntax_weights,
	upscaler_model_path,
	upscaler_increases_size,
	esrgan_tile,
	esrgan_tile_overlap,
	hires_steps,
	hires_denoising_strength,
	hires_sampler,
	hires_prompt,
	hires_negative_prompt,
	hires_before_adetailer,
	hires_after_adetailer,
	loop_generation,
	leave_progress_bar,
	disable_progress_bar,
	image_previews,
	display_images,
	save_generated_images,
	image_storage_location,
	retain_compel_previous_load,
	retain_detailfix_model_previous_load,
	retain_hires_model_previous_load,
	t2i_adapter_preprocessor,
	t2i_adapter_conditioning_scale,
	t2i_adapter_conditioning_factor,
	xformers_memory_efficient_attention,
	freeu,
	generator_in_cpu,
	adetailer_inpaint_only,
	adetailer_verbose,
	adetailer_sampler,
	adetailer_active_a,
	prompt_ad_a,
	negative_prompt_ad_a,
	strength_ad_a,
	face_detector_ad_a,
	person_detector_ad_a,
	hand_detector_ad_a,
	mask_dilation_a,
	mask_blur_a,
	mask_padding_a,
	adetailer_active_b,
	prompt_ad_b,
	negative_prompt_ad_b,
	strength_ad_b,
	face_detector_ad_b,
	person_detector_ad_b,
	hand_detector_ad_b,
	mask_dilation_b,
	mask_blur_b,
	mask_padding_b,
	):

	vae_model = vae_model if vae_model != "None" else None
	loras_list = [lora1, lora2, lora3, lora4, lora5]

	if model_name in model_list:
	model_is_xl = "xl" in model_name.lower()
	sdxl_in_vae = vae_model and "sdxl" in vae_model.lower()
	model_type = "SDXL" if model_is_xl else "SD 1.5"
	incompatible_vae = (model_is_xl and vae_model and not sdxl_in_vae) or (not model_is_xl and sdxl_in_vae)

	if incompatible_vae:
	gr.Info(
	f"The selected VAE is for a { 'SD 1.5' if model_is_xl else 'SDXL' } model, but you"
	f" are using a { model_type } model. The default VAE "
	"will be used."
	)
	vae_model = None

	for la in loras_list:
	if la is not None and la != "None":
	lora_type = "animetarot" in la.lower()
	if (model_is_xl and lora_type) or (not model_is_xl and not lora_type):
	gr.Info(f"The LoRA {la} is for { 'SD 1.5' if model_is_xl else 'SDXL' }, but you are using { model_type }.")

	task = task_stablepy[task]

	# First load
	model_precision = torch.float16
	if not self.model:
	from stablepy import Model_Diffusers

	print("Loading model...")
	self.model = Model_Diffusers(
	base_model_id=model_name,
	task_name=task,
	vae_model=vae_model if vae_model != "None" else None,
	type_model_precision=model_precision
	)

	if task != "txt2img" and not image_control:
	raise ValueError("No control image found: To use this function, you have to upload an image in 'Image ControlNet/Inpaint/Img2img'")

	if task == "inpaint" and not image_mask:
	raise ValueError("No mask image found: Specify one in 'Image Mask'")

	if upscaler_model_path in [None, "Lanczos", "Nearest"]:
	upscaler_model = upscaler_model_path
	else:
	directory_upscalers = 'upscalers'
	os.makedirs(directory_upscalers, exist_ok=True)

	url_upscaler = upscaler_dict_gui[upscaler_model_path]

	if not os.path.exists(f"./upscalers/{url_upscaler.split('/')[-1]}"):
	download_things(directory_upscalers, url_upscaler, hf_token)

	upscaler_model = f"./upscalers/{url_upscaler.split('/')[-1]}"

	logging.getLogger("ultralytics").setLevel(logging.INFO if adetailer_verbose else logging.ERROR)

	print("Config model:", model_name, vae_model, loras_list)

	self.model.load_pipe(
	model_name,
	task_name=task,
	vae_model=vae_model if vae_model != "None" else None,
	type_model_precision=model_precision
	)

	if textual_inversion and self.model.class_name == "StableDiffusionXLPipeline":
	print("No Textual inversion for SDXL")

	adetailer_params_A = {
	"face_detector_ad" : face_detector_ad_a,
	"person_detector_ad" : person_detector_ad_a,
	"hand_detector_ad" : hand_detector_ad_a,
	"prompt": prompt_ad_a,
	"negative_prompt" : negative_prompt_ad_a,
	"strength" : strength_ad_a,
	# "image_list_task" : None,
	"mask_dilation" : mask_dilation_a,
	"mask_blur" : mask_blur_a,
	"mask_padding" : mask_padding_a,
	"inpaint_only" : adetailer_inpaint_only,
	"sampler" : adetailer_sampler,
	}

	adetailer_params_B = {
	"face_detector_ad" : face_detector_ad_b,
	"person_detector_ad" : person_detector_ad_b,
	"hand_detector_ad" : hand_detector_ad_b,
	"prompt": prompt_ad_b,
	"negative_prompt" : negative_prompt_ad_b,
	"strength" : strength_ad_b,
	# "image_list_task" : None,
	"mask_dilation" : mask_dilation_b,
	"mask_blur" : mask_blur_b,
	"mask_padding" : mask_padding_b,
	}
	pipe_params = {
	"prompt": prompt,
	"negative_prompt": neg_prompt,
	"img_height": img_height,
	"img_width": img_width,
	"num_images": num_images,
	"num_steps": steps,
	"guidance_scale": cfg,
	"clip_skip": clip_skip,
	"seed": seed,
	"image": image_control,
	"preprocessor_name": preprocessor_name,
	"preprocess_resolution": preprocess_resolution,
	"image_resolution": image_resolution,
	"style_prompt": style_prompt if style_prompt else "",
	"style_json_file": "",
	"image_mask": image_mask, # only for Inpaint
	"strength": strength, # only for Inpaint or ...
	"low_threshold": low_threshold,
	"high_threshold": high_threshold,
	"value_threshold": value_threshold,
	"distance_threshold": distance_threshold,
	"lora_A": lora1 if lora1 != "None" else None,
	"lora_scale_A": lora_scale1,
	"lora_B": lora2 if lora2 != "None" else None,
	"lora_scale_B": lora_scale2,
	"lora_C": lora3 if lora3 != "None" else None,
	"lora_scale_C": lora_scale3,
	"lora_D": lora4 if lora4 != "None" else None,
	"lora_scale_D": lora_scale4,
	"lora_E": lora5 if lora5 != "None" else None,
	"lora_scale_E": lora_scale5,
	"textual_inversion": embed_list if textual_inversion and self.model.class_name != "StableDiffusionXLPipeline" else [],
	"syntax_weights": syntax_weights, # "Classic"
	"sampler": sampler,
	"xformers_memory_efficient_attention": xformers_memory_efficient_attention,
	"gui_active": True,
	"loop_generation": loop_generation,
	"controlnet_conditioning_scale": float(controlnet_output_scaling_in_unet),
	"control_guidance_start": float(controlnet_start_threshold),
	"control_guidance_end": float(controlnet_stop_threshold),
	"generator_in_cpu": generator_in_cpu,
	"FreeU": freeu,
	"adetailer_A": adetailer_active_a,
	"adetailer_A_params": adetailer_params_A,
	"adetailer_B": adetailer_active_b,
	"adetailer_B_params": adetailer_params_B,
	"leave_progress_bar": leave_progress_bar,
	"disable_progress_bar": disable_progress_bar,
	"image_previews": image_previews,
	"display_images": display_images,
	"save_generated_images": save_generated_images,
	"image_storage_location": image_storage_location,
	"retain_compel_previous_load": retain_compel_previous_load,
	"retain_detailfix_model_previous_load": retain_detailfix_model_previous_load,
	"retain_hires_model_previous_load": retain_hires_model_previous_load,
	"t2i_adapter_preprocessor": t2i_adapter_preprocessor,
	"t2i_adapter_conditioning_scale": float(t2i_adapter_conditioning_scale),
	"t2i_adapter_conditioning_factor": float(t2i_adapter_conditioning_factor),
	"upscaler_model_path": upscaler_model,
	"upscaler_increases_size": upscaler_increases_size,
	"esrgan_tile": esrgan_tile,
	"esrgan_tile_overlap": esrgan_tile_overlap,
	"hires_steps": hires_steps,
	"hires_denoising_strength": hires_denoising_strength,
	"hires_prompt": hires_prompt,
	"hires_negative_prompt": hires_negative_prompt,
	"hires_sampler": hires_sampler,
	"hires_before_adetailer": hires_before_adetailer,
	"hires_after_adetailer": hires_after_adetailer
	}

	# print(pipe_params)

	if (
	(img_height > 1700 and img_width > 1700)
	or (num_images > 1 and img_height>1048 and img_width>1048)
	or (num_images > 1 and upscaler_model)
	or (num_images > 1 and adetailer_active_a or num_images > 1 and adetailer_active_b)
	or (num_images > 1 and steps>50)
	or (adetailer_active_a and adetailer_active_b)
	or (upscaler_model and upscaler_increases_size > 1.7)
	or (steps > 75)
	or (image_resolution > 1048)
	):
	print("Inference 2")
	return self.infer(self.model, pipe_params)

	print("Inference 1")
	return self.infer_short(self.model, pipe_params)


	sd_gen = GuiSD()

	CSS ="""
	.contain { display: flex; flex-direction: column; }
	#component-0 { height: 100%; }
	#gallery { flex-grow: 1; }
	"""

	sdxl_task = task_model_list[:3] + task_model_list[3:8]
	sd_task = task_model_list[:3] + task_model_list[8:]


	def update_task_options(model_name, task_name):
	if model_name in model_list:
	if "xl" in model_name.lower():
	new_choices = sdxl_task
	else:
	new_choices = sd_task

	if task_name not in new_choices:
	task_name = "txt2img"

	return gr.update(value=task_name, choices=new_choices)
	else:
	return gr.update(value=task_name, choices=task_model_list)


	with gr.Blocks(theme="NoCrypt/miku", css=CSS) as app:
	gr.Markdown("# 🧩 DiffuseCraft")
	gr.Markdown(
	f"""
	### This demo uses [diffusers](https://github.com/huggingface/diffusers) to perform different tasks in image generation.
	"""
	)
	with gr.Tab("Generation"):
	with gr.Row():

	with gr.Column(scale=2):

	task_gui = gr.Dropdown(label="Task", choices=sdxl_task, value=task_model_list[0])
	model_name_gui = gr.Dropdown(label="Model", choices=model_list, value=model_list[0], allow_custom_value=True)
	prompt_gui = gr.Textbox(lines=5, placeholder="Enter prompt", label="Prompt")
	neg_prompt_gui = gr.Textbox(lines=3, placeholder="Enter Neg prompt", label="Negative prompt")
	generate_button = gr.Button(value="GENERATE", variant="primary")

	model_name_gui.change(
	update_task_options,
	[model_name_gui, task_gui],
	[task_gui],
	)

	result_images = gr.Gallery(
	label="Generated images",
	show_label=False,
	elem_id="gallery",
	columns=[2],
	rows=[2],
	object_fit="contain",
	# height="auto",
	interactive=False,
	preview=False,
	selected_index=50,
	)

	with gr.Column(scale=1):
	steps_gui = gr.Slider(minimum=1, maximum=100, step=1, value=30, label="Steps")
	cfg_gui = gr.Slider(minimum=0, maximum=30, step=0.5, value=7.5, label="CFG")
	sampler_gui = gr.Dropdown(label="Sampler", choices=scheduler_names, value="Euler a")
	img_width_gui = gr.Slider(minimum=64, maximum=4096, step=8, value=1024, label="Img Width")
	img_height_gui = gr.Slider(minimum=64, maximum=4096, step=8, value=1024, label="Img Height")
	clip_skip_gui = gr.Checkbox(value=True, label="Layer 2 Clip Skip")
	free_u_gui = gr.Checkbox(value=True, label="FreeU")
	seed_gui = gr.Number(minimum=-1, maximum=9999999999, value=-1, label="Seed")
	num_images_gui = gr.Slider(minimum=1, maximum=4, step=1, value=1, label="Images")
	prompt_s_options = [("Compel (default) format: (word)weight", "Compel"), ("Classic (sd1.5 long prompts) format: (word:weight)", "Classic")]
	prompt_syntax_gui = gr.Dropdown(label="Prompt Syntax", choices=prompt_s_options, value=prompt_s_options[0][1])
	vae_model_gui = gr.Dropdown(label="VAE Model", choices=vae_model_list)

	with gr.Accordion("ControlNet / Img2img / Inpaint", open=False, visible=True):
	image_control = gr.Image(label="Image ControlNet/Inpaint/Img2img", type="filepath")
	image_mask_gui = gr.Image(label="Image Mask", type="filepath")
	strength_gui = gr.Slider(
	minimum=0.01, maximum=1.0, step=0.01, value=0.55, label="Strength",
	info="This option adjusts the level of changes for img2img and inpainting."
	)
	image_resolution_gui = gr.Slider(minimum=64, maximum=2048, step=64, value=1024, label="Image Resolution")
	preprocessor_name_gui = gr.Dropdown(label="Preprocessor Name", choices=preprocessor_controlnet["canny"])

	def change_preprocessor_choices(task):
	task = task_stablepy[task]
	if task in preprocessor_controlnet.keys():
	choices_task = preprocessor_controlnet[task]
	else:
	choices_task = preprocessor_controlnet["canny"]
	return gr.update(choices=choices_task, value=choices_task[0])

	task_gui.change(
	change_preprocessor_choices,
	[task_gui],
	[preprocessor_name_gui],
	)
	preprocess_resolution_gui = gr.Slider(minimum=64, maximum=2048, step=64, value=512, label="Preprocess Resolution")
	low_threshold_gui = gr.Slider(minimum=1, maximum=255, step=1, value=100, label="Canny low threshold")
	high_threshold_gui = gr.Slider(minimum=1, maximum=255, step=1, value=200, label="Canny high threshold")
	value_threshold_gui = gr.Slider(minimum=1, maximum=2.0, step=0.01, value=0.1, label="Hough value threshold (MLSD)")
	distance_threshold_gui = gr.Slider(minimum=1, maximum=20.0, step=0.01, value=0.1, label="Hough distance threshold (MLSD)")
	control_net_output_scaling_gui = gr.Slider(minimum=0, maximum=5.0, step=0.1, value=1, label="ControlNet Output Scaling in UNet")
	control_net_start_threshold_gui = gr.Slider(minimum=0, maximum=1, step=0.01, value=0, label="ControlNet Start Threshold (%)")
	control_net_stop_threshold_gui = gr.Slider(minimum=0, maximum=1, step=0.01, value=1, label="ControlNet Stop Threshold (%)")

	with gr.Accordion("T2I adapter", open=False, visible=True):
	t2i_adapter_preprocessor_gui = gr.Checkbox(value=True, label="T2i Adapter Preprocessor")
	adapter_conditioning_scale_gui = gr.Slider(minimum=0, maximum=5., step=0.1, value=1, label="Adapter Conditioning Scale")
	adapter_conditioning_factor_gui = gr.Slider(minimum=0, maximum=1., step=0.01, value=0.55, label="Adapter Conditioning Factor (%)")

	with gr.Accordion("LoRA", open=False, visible=True):
	lora1_gui = gr.Dropdown(label="Lora1", choices=lora_model_list)
	lora_scale_1_gui = gr.Slider(minimum=-2, maximum=2, step=0.01, value=0.33, label="Lora Scale 1")
	lora2_gui = gr.Dropdown(label="Lora2", choices=lora_model_list)
	lora_scale_2_gui = gr.Slider(minimum=-2, maximum=2, step=0.01, value=0.33, label="Lora Scale 2")
	lora3_gui = gr.Dropdown(label="Lora3", choices=lora_model_list)
	lora_scale_3_gui = gr.Slider(minimum=-2, maximum=2, step=0.01, value=0.33, label="Lora Scale 3")
	lora4_gui = gr.Dropdown(label="Lora4", choices=lora_model_list)
	lora_scale_4_gui = gr.Slider(minimum=-2, maximum=2, step=0.01, value=0.33, label="Lora Scale 4")
	lora5_gui = gr.Dropdown(label="Lora5", choices=lora_model_list)
	lora_scale_5_gui = gr.Slider(minimum=-2, maximum=2, step=0.01, value=0.33, label="Lora Scale 5")

	with gr.Accordion("Styles", open=False, visible=True):

	try:
	style_names_found = sd_gen.model.STYLE_NAMES
	except:
	style_names_found = STYLE_NAMES

	style_prompt_gui = gr.Dropdown(
	style_names_found,
	multiselect=True,
	value=None,
	label="Style Prompt",
	interactive=True,
	)
	style_json_gui = gr.File(label="Style JSON File")
	style_button = gr.Button("Load styles")

	def load_json_style_file(json):
	if not sd_gen.model:
	gr.Info("First load the model")
	return gr.update(value=None, choices=STYLE_NAMES)

	sd_gen.model.load_style_file(json)
	gr.Info(f"{len(sd_gen.model.STYLE_NAMES)} styles loaded")
	return gr.update(value=None, choices=sd_gen.model.STYLE_NAMES)

	style_button.click(load_json_style_file, [style_json_gui], [style_prompt_gui])

	with gr.Accordion("Textual inversion", open=False, visible=False):
	active_textual_inversion_gui = gr.Checkbox(value=False, label="Active Textual Inversion in prompt")

	with gr.Accordion("Hires fix", open=False, visible=True):

	upscaler_keys = list(upscaler_dict_gui.keys())

	upscaler_model_path_gui = gr.Dropdown(label="Upscaler", choices=upscaler_keys, value=upscaler_keys[0])
	upscaler_increases_size_gui = gr.Slider(minimum=1.1, maximum=6., step=0.1, value=1.5, label="Upscale by")
	esrgan_tile_gui = gr.Slider(minimum=0, value=100, maximum=500, step=1, label="ESRGAN Tile")
	esrgan_tile_overlap_gui = gr.Slider(minimum=1, maximum=200, step=1, value=10, label="ESRGAN Tile Overlap")
	hires_steps_gui = gr.Slider(minimum=0, value=30, maximum=100, step=1, label="Hires Steps")
	hires_denoising_strength_gui = gr.Slider(minimum=0.1, maximum=1.0, step=0.01, value=0.55, label="Hires Denoising Strength")
	hires_sampler_gui = gr.Dropdown(label="Hires Sampler", choices=["Use same sampler"] + scheduler_names[:-1], value="Use same sampler")
	hires_prompt_gui = gr.Textbox(label="Hires Prompt", placeholder="Main prompt will be use", lines=3)
	hires_negative_prompt_gui = gr.Textbox(label="Hires Negative Prompt", placeholder="Main negative prompt will be use", lines=3)

	with gr.Accordion("Detailfix", open=False, visible=True):

	# Adetailer Inpaint Only
	adetailer_inpaint_only_gui = gr.Checkbox(label="Inpaint only", value=True)

	# Adetailer Verbose
	adetailer_verbose_gui = gr.Checkbox(label="Verbose", value=False)

	# Adetailer Sampler
	adetailer_sampler_options = ["Use same sampler"] + scheduler_names[:-1]
	adetailer_sampler_gui = gr.Dropdown(label="Adetailer sampler:", choices=adetailer_sampler_options, value="Use same sampler")

	with gr.Accordion("Detailfix A", open=False, visible=True):
	# Adetailer A
	adetailer_active_a_gui = gr.Checkbox(label="Enable Adetailer A", value=False)
	prompt_ad_a_gui = gr.Textbox(label="Main prompt", placeholder="Main prompt will be use", lines=3)
	negative_prompt_ad_a_gui = gr.Textbox(label="Negative prompt", placeholder="Main negative prompt will be use", lines=3)
	strength_ad_a_gui = gr.Number(label="Strength:", value=0.35, step=0.01, minimum=0.01, maximum=1.0)
	face_detector_ad_a_gui = gr.Checkbox(label="Face detector", value=True)
	person_detector_ad_a_gui = gr.Checkbox(label="Person detector", value=True)
	hand_detector_ad_a_gui = gr.Checkbox(label="Hand detector", value=False)
	mask_dilation_a_gui = gr.Number(label="Mask dilation:", value=4, minimum=1)
	mask_blur_a_gui = gr.Number(label="Mask blur:", value=4, minimum=1)
	mask_padding_a_gui = gr.Number(label="Mask padding:", value=32, minimum=1)

	with gr.Accordion("Detailfix B", open=False, visible=True):
	# Adetailer B
	adetailer_active_b_gui = gr.Checkbox(label="Enable Adetailer B", value=False)
	prompt_ad_b_gui = gr.Textbox(label="Main prompt", placeholder="Main prompt will be use", lines=3)
	negative_prompt_ad_b_gui = gr.Textbox(label="Negative prompt", placeholder="Main negative prompt will be use", lines=3)
	strength_ad_b_gui = gr.Number(label="Strength:", value=0.35, step=0.01, minimum=0.01, maximum=1.0)
	face_detector_ad_b_gui = gr.Checkbox(label="Face detector", value=True)
	person_detector_ad_b_gui = gr.Checkbox(label="Person detector", value=True)
	hand_detector_ad_b_gui = gr.Checkbox(label="Hand detector", value=False)
	mask_dilation_b_gui = gr.Number(label="Mask dilation:", value=4, minimum=1)
	mask_blur_b_gui = gr.Number(label="Mask blur:", value=4, minimum=1)
	mask_padding_b_gui = gr.Number(label="Mask padding:", value=32, minimum=1)

	with gr.Accordion("Other settings", open=False, visible=True):
	hires_before_adetailer_gui = gr.Checkbox(value=False, label="Hires Before Adetailer")
	hires_after_adetailer_gui = gr.Checkbox(value=True, label="Hires After Adetailer")
	generator_in_cpu_gui = gr.Checkbox(value=False, label="Generator in CPU")

	with gr.Accordion("More settings", open=False, visible=False):
	loop_generation_gui = gr.Slider(minimum=1, value=1, label="Loop Generation")
	leave_progress_bar_gui = gr.Checkbox(value=True, label="Leave Progress Bar")
	disable_progress_bar_gui = gr.Checkbox(value=False, label="Disable Progress Bar")
	image_previews_gui = gr.Checkbox(value=False, label="Image Previews")
	display_images_gui = gr.Checkbox(value=False, label="Display Images")
	save_generated_images_gui = gr.Checkbox(value=False, label="Save Generated Images")
	image_storage_location_gui = gr.Textbox(value="./images", label="Image Storage Location")
	retain_compel_previous_load_gui = gr.Checkbox(value=False, label="Retain Compel Previous Load")
	retain_detailfix_model_previous_load_gui = gr.Checkbox(value=False, label="Retain Detailfix Model Previous Load")
	retain_hires_model_previous_load_gui = gr.Checkbox(value=False, label="Retain Hires Model Previous Load")
	xformers_memory_efficient_attention_gui = gr.Checkbox(value=False, label="Xformers Memory Efficient Attention")

	with gr.Accordion("Examples", open=False, visible=True):
	gr.Examples(
	examples=[
	[
	"1girl, souryuu asuka langley, neon genesis evangelion, plugsuit, pilot suit, red bodysuit, sitting, crossing legs, black eye patch, cat hat, throne, symmetrical, looking down, from bottom, looking at viewer, outdoors, masterpiece, best quality, very aesthetic, absurdres",
	"nsfw, lowres, (bad), text, error, fewer, extra, missing, worst quality, jpeg artifacts, low quality, watermark, unfinished, displeasing, oldest, early, chromatic aberration, signature, extra digits, artistic error, username, scan, [abstract]",
	1,
	30,
	7.5,
	True,
	-1,
	None,
	1.0,
	None,
	1.0,
	None,
	1.0,
	None,
	1.0,
	None,
	1.0,
	"Euler a",
	1152,
	896,
	"cagliostrolab/animagine-xl-3.1",
	None, # vae
	"txt2img",
	None, # img conttol
	"Canny", # preprocessor
	512, # preproc resolution
	1024, # img resolution
	None, # Style prompt
	None, # Style json
	None, # img Mask
	0.35, # strength
	100, # low th canny
	200, # high th canny
	0.1, # value mstd
	0.1, # distance mstd
	1.0, # cn scale
	0., # cn start
	1., # cn end
	False, # ti
	"Classic",
	"Nearest",
	],
	[
	"score_9, score_8_up, score_8, medium breasts, cute, eyelashes , princess Zelda OOT, cute small face, long hair, crown braid, hairclip, pointy ears, soft curvy body, solo, looking at viewer, smile, blush, white dress, medium body, (((holding the Master Sword))), standing, deep forest in the background",
	"score_6, score_5, score_4, busty, ugly face, mutated hands, low res, blurry face, black and white,",
	1,
	30,
	5.,
	True,
	-1,
	None,
	1.0,
	None,
	1.0,
	None,
	1.0,
	None,
	1.0,
	None,
	1.0,
	"DPM++ 2M Karras",
	1024,
	1024,
	"kitty7779/ponyDiffusionV6XL",
	None, # vae
	"txt2img",
	None, # img conttol
	"Canny", # preprocessor
	512, # preproc resolution
	1024, # img resolution
	None, # Style prompt
	None, # Style json
	None, # img Mask
	0.35, # strength
	100, # low th canny
	200, # high th canny
	0.1, # value mstd
	0.1, # distance mstd
	1.0, # cn scale
	0., # cn start
	1., # cn end
	False, # ti
	"Classic",
	"Nearest",
	],
	[
	"((masterpiece)), best quality, blonde disco girl, detailed face, realistic face, realistic hair, dynamic pose, pink pvc, intergalactic disco background, pastel lights, dynamic contrast, airbrush, fine detail, 70s vibe, midriff ",
	"(worst quality:1.2), (bad quality:1.2), (poor quality:1.2), (missing fingers:1.2), bad-artist-anime, bad-artist, bad-picture-chill-75v",
	1,
	48,
	3.5,
	True,
	-1,
	None,
	1.0,
	None,
	1.0,
	None,
	1.0,
	None,
	1.0,
	None,
	1.0,
	"DPM++ 2M SDE Lu",
	1024,
	1024,
	"misri/epicrealismXL_v7FinalDestination",
	None, # vae
	"sdxl_canny T2I Adapter",
	"image.webp", # img conttol
	"Canny", # preprocessor
	1024, # preproc resolution
	1024, # img resolution
	None, # Style prompt
	None, # Style json
	None, # img Mask
	0.35, # strength
	100, # low th canny
	200, # high th canny
	0.1, # value mstd
	0.1, # distance mstd
	1.0, # cn scale
	0., # cn start
	1., # cn end
	False, # ti
	"Classic",
	None,
	],
	[
	"masterpiece,high resolution,japanese town street background,fantasy world,magical,mountains forest background,stairs,(torii:1.2),masterpiece,cinematic,visual key,best quality,by hayao miyazaki,by makoto shinkai,soft dim lighting,pastel colors,night,stars",
	"(low quality, worst quality:1.4), (bad_prompt:0.8), (monochrome:1.1), (greyscale), painting, cartoon, comic, anime, manga, drawing, 2d, flat, crayon, sketch",
	1,
	50,
	4.,
	True,
	-1,
	None,
	1.0,
	None,
	1.0,
	None,
	1.0,
	None,
	1.0,
	None,
	1.0,
	"DPM++ 2M Karras",
	1024,
	1024,
	"misri/juggernautXL_juggernautX",
	None, # vae
	"txt2img",
	None, # img conttol
	"Canny", # preprocessor
	512, # preproc resolution
	1024, # img resolution
	None, # Style prompt
	None, # Style json
	None, # img Mask
	0.35, # strength
	100, # low th canny
	200, # high th canny
	0.1, # value mstd
	0.1, # distance mstd
	1.0, # cn scale
	0., # cn start
	1., # cn end
	False, # ti
	"Classic",
	None,
	],
	[
	"1girl, solo, black dress, black hair, black theme, dress, eyelashes, jewelry, makeup, parted lips, purple eyes, ring, short hair, silk, silver hair, snake, masterpiece, best quality",
	"(low quality, worst quality:1.4), (bad_prompt:0.8), (monochrome:1.1), (greyscale), painting, cartoon, comic, anime, manga, drawing, 2d, flat, crayon, sketch",
	1,
	50,
	4.,
	True,
	-1,
	None,
	1.0,
	None,
	1.0,
	None,
	1.0,
	None,
	1.0,
	None,
	1.0,
	"DPM++ 2M Karras",
	1344,
	896,
	"misri/anima_pencil-XL-v4.0.0",
	None, # vae
	"txt2img",
	None, # img conttol
	"Canny", # preprocessor
	512, # preproc resolution
	1024, # img resolution
	None, # Style prompt
	None, # Style json
	None, # img Mask
	0.35, # strength
	100, # low th canny
	200, # high th canny
	0.1, # value mstd
	0.1, # distance mstd
	1.0, # cn scale
	0., # cn start
	1., # cn end
	False, # ti
	"Classic",
	None,
	],
	[
	"1girl,face,curly hair,red hair,white background,",
	"(worst quality:2),(low quality:2),(normal quality:2),lowres,watermark,",
	1,
	38,
	5.,
	True,
	-1,
	None,
	1.0,
	None,
	1.0,
	None,
	1.0,
	None,
	1.0,
	None,
	1.0,
	"DPM++ 2M SDE Karras",
	512,
	512,
	"digiplay/majicMIX_realistic_v7",
	None, # vae
	"sd_canny ControlNet",
	"image.webp", # img conttol
	"Canny", # preprocessor
	512, # preproc resolution
	1024, # img resolution
	None, # Style prompt
	None, # Style json
	None, # img Mask
	0.35, # strength
	100, # low th canny
	200, # high th canny
	0.1, # value mstd
	0.1, # distance mstd
	1.0, # cn scale
	0., # cn start
	1., # cn end
	False, # ti
	"Compel",
	"Nearest",
	],
	],
	fn=sd_gen.generate_pipeline,
	inputs=[
	prompt_gui,
	neg_prompt_gui,
	num_images_gui,
	steps_gui,
	cfg_gui,
	clip_skip_gui,
	seed_gui,
	lora1_gui,
	lora_scale_1_gui,
	lora2_gui,
	lora_scale_2_gui,
	lora3_gui,
	lora_scale_3_gui,
	lora4_gui,
	lora_scale_4_gui,
	lora5_gui,
	lora_scale_5_gui,
	sampler_gui,
	img_height_gui,
	img_width_gui,
	model_name_gui,
	vae_model_gui,
	task_gui,
	image_control,
	preprocessor_name_gui,
	preprocess_resolution_gui,
	image_resolution_gui,
	style_prompt_gui,
	style_json_gui,
	image_mask_gui,
	strength_gui,
	low_threshold_gui,
	high_threshold_gui,
	value_threshold_gui,
	distance_threshold_gui,
	control_net_output_scaling_gui,
	control_net_start_threshold_gui,
	control_net_stop_threshold_gui,
	active_textual_inversion_gui,
	prompt_syntax_gui,
	upscaler_model_path_gui,
	],
	outputs=[result_images],
	cache_examples=False,
	)

	with gr.Tab("Inpaint mask maker", render=True):

	def create_mask_now(img, invert):
	import numpy as np
	import time

	time.sleep(0.5)

	transparent_image = img["layers"][0]

	# Extract the alpha channel
	alpha_channel = np.array(transparent_image)[:, :, 3]

	# Create a binary mask by thresholding the alpha channel
	binary_mask = alpha_channel > 1

	if invert:
	print("Invert")
	# Invert the binary mask so that the drawn shape is white and the rest is black
	binary_mask = np.invert(binary_mask)

	# Convert the binary mask to a 3-channel RGB mask
	rgb_mask = np.stack((binary_mask,) * 3, axis=-1)

	# Convert the mask to uint8
	rgb_mask = rgb_mask.astype(np.uint8) * 255

	return img["background"], rgb_mask

	with gr.Row():
	with gr.Column(scale=2):
	# image_base = gr.ImageEditor(label="Base image", show_label=True, brush=gr.Brush(colors=["#000000"]))
	image_base = gr.ImageEditor(
	sources=["upload", "clipboard"],
	# crop_size="1:1",
	# enable crop (or disable it)
	# transforms=["crop"],
	brush=gr.Brush(
	default_size="16", # or leave it as 'auto'
	color_mode="fixed", # 'fixed' hides the user swatches and colorpicker, 'defaults' shows it
	#default_color="black", # html names are supported
	colors=[
	"rgba(0, 0, 0, 1)", # rgb(a)
	"rgba(0, 0, 0, 0.1)",
	"rgba(255, 255, 255, 0.1)",
	# "hsl(360, 120, 120)" # in fact any valid colorstring
	]
	),
	eraser=gr.Eraser(default_size="16")
	)
	invert_mask = gr.Checkbox(value=False, label="Invert mask")
	btn = gr.Button("Create mask")
	with gr.Column(scale=1):
	img_source = gr.Image(interactive=False)
	img_result = gr.Image(label="Mask image", show_label=True, interactive=False)
	btn_send = gr.Button("Send to the first tab")

	btn.click(create_mask_now, [image_base, invert_mask], [img_source, img_result])

	def send_img(img_source, img_result):
	return img_source, img_result
	btn_send.click(send_img, [img_source, img_result], [image_control, image_mask_gui])

	generate_button.click(
	fn=sd_gen.generate_pipeline,
	inputs=[
	prompt_gui,
	neg_prompt_gui,
	num_images_gui,
	steps_gui,
	cfg_gui,
	clip_skip_gui,
	seed_gui,
	lora1_gui,
	lora_scale_1_gui,
	lora2_gui,
	lora_scale_2_gui,
	lora3_gui,
	lora_scale_3_gui,
	lora4_gui,
	lora_scale_4_gui,
	lora5_gui,
	lora_scale_5_gui,
	sampler_gui,
	img_height_gui,
	img_width_gui,
	model_name_gui,
	vae_model_gui,
	task_gui,
	image_control,
	preprocessor_name_gui,
	preprocess_resolution_gui,
	image_resolution_gui,
	style_prompt_gui,
	style_json_gui,
	image_mask_gui,
	strength_gui,
	low_threshold_gui,
	high_threshold_gui,
	value_threshold_gui,
	distance_threshold_gui,
	control_net_output_scaling_gui,
	control_net_start_threshold_gui,
	control_net_stop_threshold_gui,
	active_textual_inversion_gui,
	prompt_syntax_gui,
	upscaler_model_path_gui,
	upscaler_increases_size_gui,
	esrgan_tile_gui,
	esrgan_tile_overlap_gui,
	hires_steps_gui,
	hires_denoising_strength_gui,
	hires_sampler_gui,
	hires_prompt_gui,
	hires_negative_prompt_gui,
	hires_before_adetailer_gui,
	hires_after_adetailer_gui,
	loop_generation_gui,
	leave_progress_bar_gui,
	disable_progress_bar_gui,
	image_previews_gui,
	display_images_gui,
	save_generated_images_gui,
	image_storage_location_gui,
	retain_compel_previous_load_gui,
	retain_detailfix_model_previous_load_gui,
	retain_hires_model_previous_load_gui,
	t2i_adapter_preprocessor_gui,
	adapter_conditioning_scale_gui,
	adapter_conditioning_factor_gui,
	xformers_memory_efficient_attention_gui,
	free_u_gui,
	generator_in_cpu_gui,
	adetailer_inpaint_only_gui,
	adetailer_verbose_gui,
	adetailer_sampler_gui,
	adetailer_active_a_gui,
	prompt_ad_a_gui,
	negative_prompt_ad_a_gui,
	strength_ad_a_gui,
	face_detector_ad_a_gui,
	person_detector_ad_a_gui,
	hand_detector_ad_a_gui,
	mask_dilation_a_gui,
	mask_blur_a_gui,
	mask_padding_a_gui,
	adetailer_active_b_gui,
	prompt_ad_b_gui,
	negative_prompt_ad_b_gui,
	strength_ad_b_gui,
	face_detector_ad_b_gui,
	person_detector_ad_b_gui,
	hand_detector_ad_b_gui,
	mask_dilation_b_gui,
	mask_blur_b_gui,
	mask_padding_b_gui,
	],
	outputs=[result_images],
	queue=True,
	)

	app.queue()

	app.launch(
	show_error=True,
	debug=True,
	)