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Running
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Zero
| 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 | |
| import random | |
| from stablepy import ( | |
| CONTROLNET_MODEL_IDS, | |
| VALID_TASKS, | |
| T2I_PREPROCESSOR_NAME, | |
| FLASH_LORA, | |
| SCHEDULER_CONFIG_MAP, | |
| scheduler_names, | |
| IP_ADAPTER_MODELS, | |
| IP_ADAPTERS_SD, | |
| IP_ADAPTERS_SDXL, | |
| REPO_IMAGE_ENCODER, | |
| ALL_PROMPT_WEIGHT_OPTIONS, | |
| SD15_TASKS, | |
| SDXL_TASKS, | |
| ) | |
| import urllib.parse | |
| from config import ( | |
| MINIMUM_IMAGE_NUMBER, | |
| MAXIMUM_IMAGE_NUMBER, | |
| DEFAULT_NEGATIVE_PROMPT | |
| ) | |
| 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", | |
| "Lineart (anime)", | |
| "None", | |
| "None (anime)", | |
| ], | |
| "shuffle": [ | |
| "ContentShuffle", | |
| "None", | |
| ], | |
| "canny": [ | |
| "Canny" | |
| ], | |
| "mlsd": [ | |
| "MLSD" | |
| ], | |
| "ip2p": [ | |
| "ip2p" | |
| ] | |
| } | |
| task_stablepy = { | |
| 'txt2img': 'txt2img', | |
| 'img2img': 'img2img', | |
| 'inpaint': 'inpaint', | |
| # 'canny T2I Adapter': 'sdxl_canny_t2i', # NO HAVE STEP CALLBACK PARAMETERS SO NOT WORKS WITH DIFFUSERS 0.29.0 | |
| # 'sketch T2I Adapter': 'sdxl_sketch_t2i', | |
| # 'lineart T2I Adapter': 'sdxl_lineart_t2i', | |
| # 'depth-midas T2I Adapter': 'sdxl_depth-midas_t2i', | |
| # 'openpose T2I Adapter': 'sdxl_openpose_t2i', | |
| 'openpose ControlNet': 'openpose', | |
| 'canny ControlNet': 'canny', | |
| 'mlsd ControlNet': 'mlsd', | |
| 'scribble ControlNet': 'scribble', | |
| 'softedge ControlNet': 'softedge', | |
| 'segmentation ControlNet': 'segmentation', | |
| 'depth ControlNet': 'depth', | |
| 'normalbae ControlNet': 'normalbae', | |
| 'lineart ControlNet': 'lineart', | |
| # 'lineart_anime ControlNet': 'lineart_anime', | |
| 'shuffle ControlNet': 'shuffle', | |
| 'ip2p ControlNet': 'ip2p', | |
| 'optical pattern ControlNet': 'pattern', | |
| 'tile realistic': 'sdxl_tile_realistic', | |
| } | |
| task_model_list = list(task_stablepy.keys()) | |
| 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", "") | |
| # url = urllib.parse.quote(url, safe=':/') # fix encoding | |
| 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 " | |
| f"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: 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://civitai.com/api/download/models/597138, " | |
| "https://civitai.com/api/download/models/469243, " | |
| "https://civitai.com/api/download/models/609052, " | |
| "https://civitai.com/api/download/models/5036?type=Model&format=SafeTensor&size=full&fp=fp16, " | |
| "https://civitai.com/api/download/models/574369, " | |
| "https://huggingface.co/TechnoByte/MilkyWonderland/resolve/main/milkyWonderland_v40.safetensors" | |
| ) | |
| # - **Download VAEs** | |
| download_vae = ( | |
| "https://civitai.com/api/download/models/128078, " | |
| "https://huggingface.co/stabilityai/sdxl-vae/resolve/main/sdxl_vae.safetensors, " | |
| "https://civitai.com/api/download/models/403131?type=VAE&format=SafeTensor, " | |
| "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/vae-ft-mse-840000-ema-pruned_fp16.safetensors?download=true" | |
| ) | |
| # - **Download LoRAs** | |
| download_lora = ( | |
| "https://civitai.com/api/download/models/423719, " | |
| "https://civitai.com/api/download/models/50503, " | |
| "https://civitai.com/api/download/models/133160, " | |
| "https://civitai.com/api/download/models/29332, " | |
| "https://huggingface.co/Leopain/color/resolve/main/Coloring_book_-_LineArt.safetensors, " | |
| "https://civitai.com/api/download/models/135867, " | |
| "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, " | |
| "https://huggingface.co/ByteDance/Hyper-SD/resolve/main/Hyper-SD15-8steps-CFG-lora.safetensors?download=true, " | |
| "https://huggingface.co/ByteDance/Hyper-SD/resolve/main/Hyper-SDXL-8steps-CFG-lora.safetensors?download=true, " | |
| "https://civitai.com/api/download/models/30666 " | |
| ) | |
| load_diffusers_format_model = [ | |
| 'stabilityai/stable-diffusion-xl-base-1.0', | |
| 'cagliostrolab/animagine-xl-3.1', | |
| 'misri/epicrealismXL_v7FinalDestination', | |
| 'misri/juggernautXL_juggernautX', | |
| 'misri/zavychromaxl_v80', | |
| 'SG161222/RealVisXL_V4.0', | |
| 'misri/newrealityxlAllInOne_Newreality40', | |
| 'eienmojiki/Anything-XL', | |
| 'eienmojiki/Starry-XL-v5.2', | |
| 'gsdf/CounterfeitXL', | |
| 'kitty7779/ponyDiffusionV6XL', | |
| 'John6666/ebara-mfcg-pony-mix-v12-sdxl', | |
| 'John6666/t-ponynai3-v51-sdxl', | |
| 'yodayo-ai/kivotos-xl-2.0', | |
| 'yodayo-ai/holodayo-xl-2.1', | |
| 'digiplay/majicMIX_sombre_v2', | |
| 'digiplay/majicMIX_realistic_v6', | |
| 'digiplay/majicMIX_realistic_v7', | |
| 'digiplay/DreamShaper_8', | |
| 'digiplay/BeautifulArt_v1', | |
| 'digiplay/DarkSushi2.5D_v1', | |
| 'digiplay/darkphoenix3D_v1.1', | |
| 'digiplay/BeenYouLiteL11_diffusers', | |
| 'rubbrband/revAnimated_v2Rebirth', | |
| 'youknownothing/cyberrealistic_v50', | |
| 'votepurchase/counterfeitV30_v30', | |
| 'Meina/MeinaMix_V11', | |
| 'Meina/MeinaUnreal_V5', | |
| 'Meina/MeinaPastel_V7', | |
| 'rubbrband/realcartoon3d_v16', | |
| 'rubbrband/realcartoonRealistic_v14', | |
| ] | |
| 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/blob/main/bad_prompt_version2.pt', | |
| 'https://huggingface.co/embed/negative/resolve/main/EasyNegativeV2.safetensors', | |
| 'https://huggingface.co/embed/negative/resolve/main/bad-hands-5.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") | |
| def get_my_lora(link_url): | |
| for url in [url.strip() for url in link_url.split(',')]: | |
| if not os.path.exists(f"./loras/{url.split('/')[-1]}"): | |
| download_things(directory_loras, url, hf_token, CIVITAI_API_KEY) | |
| new_lora_model_list = get_model_list(directory_loras) | |
| new_lora_model_list.insert(0, "None") | |
| return gr.update( | |
| choices=new_lora_model_list | |
| ), gr.update( | |
| choices=new_lora_model_list | |
| ), gr.update( | |
| choices=new_lora_model_list | |
| ), gr.update( | |
| choices=new_lora_model_list | |
| ), gr.update( | |
| choices=new_lora_model_list | |
| ), | |
| 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) | |
| def info_html(json_data, title, subtitle): | |
| return f""" | |
| <div style='padding: 0; border-radius: 10px;'> | |
| <p style='margin: 0; font-weight: bold;'>{title}</p> | |
| <details> | |
| <summary>Details</summary> | |
| <p style='margin: 0; font-weight: bold;'>{subtitle}</p> | |
| </details> | |
| </div> | |
| """ | |
| class GuiSD: | |
| def __init__(self, stream=True): | |
| self.model = None | |
| print("Loading model...") | |
| self.model = Model_Diffusers( | |
| base_model_id="cagliostrolab/animagine-xl-3.1", | |
| task_name="txt2img", | |
| vae_model=None, | |
| type_model_precision=torch.float16, | |
| retain_task_model_in_cache=False, | |
| ) | |
| def load_new_model(self, model_name, vae_model, task, progress=gr.Progress(track_tqdm=True)): | |
| yield f"Loading model: {model_name}" | |
| vae_model = vae_model if vae_model != "None" else None | |
| 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: | |
| vae_model = None | |
| self.model.load_pipe( | |
| model_name, | |
| task_name=task_stablepy[task], | |
| vae_model=vae_model if vae_model != "None" else None, | |
| type_model_precision=torch.float16, | |
| retain_task_model_in_cache=False, | |
| ) | |
| yield f"Model loaded: {model_name}" | |
| 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, | |
| retain_task_cache_gui, | |
| image_ip1, | |
| mask_ip1, | |
| model_ip1, | |
| mode_ip1, | |
| scale_ip1, | |
| image_ip2, | |
| mask_ip2, | |
| model_ip2, | |
| mode_ip2, | |
| scale_ip2, | |
| ): | |
| vae_model = vae_model if vae_model != "None" else None | |
| loras_list = [lora1, lora2, lora3, lora4, lora5] | |
| vae_msg = f"VAE: {vae_model}" if vae_model else "" | |
| msg_lora = [] | |
| 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: | |
| msg_inc_vae = ( | |
| 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." | |
| ) | |
| gr.Info(msg_inc_vae) | |
| vae_msg = msg_inc_vae | |
| vae_model = None | |
| for la in loras_list: | |
| if la is not None and la != "None" and la in lora_model_list: | |
| print(la) | |
| lora_type = ("animetarot" in la.lower() or "Hyper-SD15-8steps".lower() in la.lower()) | |
| if (model_is_xl and lora_type) or (not model_is_xl and not lora_type): | |
| msg_inc_lora = f"The LoRA {la} is for {'SD 1.5' if model_is_xl else 'SDXL'}, but you are using {model_type}." | |
| gr.Info(msg_inc_lora) | |
| msg_lora.append(msg_inc_lora) | |
| task = task_stablepy[task] | |
| params_ip_img: list = [] | |
| params_ip_msk: list = [] | |
| params_ip_model: list = [] | |
| params_ip_mode: list = [] | |
| params_ip_scale: list = [] | |
| all_adapters = [ | |
| (image_ip1, mask_ip1, model_ip1, mode_ip1, scale_ip1), | |
| (image_ip2, mask_ip2, model_ip2, mode_ip2, scale_ip2), | |
| ] | |
| for imgip, mskip, modelip, modeip, scaleip in all_adapters: | |
| if imgip: | |
| params_ip_img.append(imgip) | |
| if mskip: | |
| params_ip_msk.append(mskip) | |
| params_ip_model.append(modelip) | |
| params_ip_mode.append(modeip) | |
| params_ip_scale.append(scaleip) | |
| # First load | |
| model_precision = torch.float16 | |
| if not self.model: | |
| from modelstream import Model_Diffusers2 | |
| print("Loading model...") | |
| self.model = Model_Diffusers2( | |
| base_model_id=model_name, | |
| task_name=task, | |
| vae_model=vae_model if vae_model != "None" else None, | |
| type_model_precision=model_precision, | |
| retain_task_model_in_cache=retain_task_cache_gui, | |
| ) | |
| 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, | |
| retain_task_model_in_cache=retain_task_cache_gui, | |
| ) | |
| 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, | |
| "ip_adapter_image": params_ip_img, | |
| "ip_adapter_mask": params_ip_msk, | |
| "ip_adapter_model": params_ip_model, | |
| "ip_adapter_mode": params_ip_mode, | |
| "ip_adapter_scale": params_ip_scale, | |
| } | |
| # print(pipe_params) | |
| random_number = random.randint(1, 100) | |
| if random_number < 25 and num_images < 3: | |
| if (not upscaler_model and | |
| steps < 45 and | |
| task in ["txt2img", "img2img"] and | |
| not adetailer_active_a and | |
| not adetailer_active_b): | |
| num_images *= 2 | |
| pipe_params["num_images"] = num_images | |
| gr.Info("Num images x 2 🎉") | |
| # Maybe fix lora issue: 'Cannot copy out of meta tensor; no data!'' | |
| self.model.pipe.to("cuda:0" if torch.cuda.is_available() else "cpu") | |
| info_state = f"PROCESSING " | |
| for img, seed, data in self.model(**pipe_params): | |
| info_state += ">" | |
| if data: | |
| info_state = f"COMPLETED. Seeds: {str(seed)}" | |
| if vae_msg: | |
| info_state = info_state + "<br>" + vae_msg | |
| if msg_lora: | |
| info_state = info_state + "<br>" + "<br>".join(msg_lora) | |
| yield img, info_state | |
| sd_gen = GuiSD() | |
| CSS = """ | |
| .contain { display: flex; flex-direction: column; } | |
| #component-0 { height: 100%; } | |
| #gallery { flex-grow: 1; } | |
| """ | |
| sdxl_task = [k for k, v in task_stablepy.items() if v in SDXL_TASKS] | |
| sd_task = [k for k, v in task_stablepy.items() if v in SD15_TASKS] | |
| 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("# 🧩 (Ivan) 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", | |
| value=DEFAULT_NEGATIVE_PROMPT | |
| ) | |
| with gr.Row(equal_height=False): | |
| set_params_gui = gr.Button(value="↙️") | |
| clear_prompt_gui = gr.Button(value="🗑️") | |
| set_random_seed = gr.Button(value="🎲") | |
| generate_button = gr.Button( | |
| value="GENERATE", | |
| variant="primary" | |
| ) | |
| model_name_gui.change( | |
| update_task_options, | |
| [model_name_gui, task_gui], | |
| [task_gui], | |
| ) | |
| load_model_gui = gr.HTML() | |
| 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, | |
| ) | |
| actual_task_info = gr.HTML() | |
| with gr.Column(scale=1): | |
| steps_gui = gr.Slider( | |
| minimum=1, | |
| maximum=100, | |
| step=1, | |
| value=43, | |
| 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="DPM++ 2M Karras") | |
| 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") | |
| seed_gui = gr.Number(minimum=-1, maximum=9999999999, value=-1, label="Seed") | |
| with gr.Row(): | |
| clip_skip_gui = gr.Checkbox(value=True, label="Layer 2 Clip Skip") | |
| free_u_gui = gr.Checkbox(value=True, label="FreeU") | |
| with gr.Row(equal_height=False): | |
| def run_set_params_gui(base_prompt): | |
| valid_receptors = { # default values | |
| "prompt": gr.update(value=base_prompt), | |
| "neg_prompt": gr.update(value=""), | |
| "Steps": gr.update(value=30), | |
| "width": gr.update(value=1024), | |
| "height": gr.update(value=1024), | |
| "Seed": gr.update(value=-1), | |
| "Sampler": gr.update(value="Euler a"), | |
| "scale": gr.update(value=7.5), # cfg | |
| "skip": gr.update(value=True), | |
| } | |
| valid_keys = list(valid_receptors.keys()) | |
| parameters = extract_parameters(base_prompt) | |
| for key, val in parameters.items(): | |
| # print(val) | |
| if key in valid_keys: | |
| if key == "Sampler": | |
| if val not in scheduler_names: | |
| continue | |
| elif key == "skip": | |
| if int(val) >= 2: | |
| val = True | |
| if key == "prompt": | |
| if ">" in val and "<" in val: | |
| val = re.sub(r'<[^>]+>', '', val) | |
| print("Removed LoRA written in the prompt") | |
| if key in ["prompt", "neg_prompt"]: | |
| val = val.strip() | |
| if key in ["Steps", "width", "height", "Seed"]: | |
| val = int(val) | |
| if key == "scale": | |
| val = float(val) | |
| if key == "Seed": | |
| continue | |
| valid_receptors[key] = gr.update(value=val) | |
| # print(val, type(val)) | |
| # print(valid_receptors) | |
| return [value for value in valid_receptors.values()] | |
| set_params_gui.click( | |
| run_set_params_gui, [prompt_gui], [ | |
| prompt_gui, | |
| neg_prompt_gui, | |
| steps_gui, | |
| img_width_gui, | |
| img_height_gui, | |
| seed_gui, | |
| sampler_gui, | |
| cfg_gui, | |
| clip_skip_gui, | |
| ], | |
| ) | |
| def run_clear_prompt_gui(): | |
| return gr.update(value=""), gr.update(value="") | |
| clear_prompt_gui.click( | |
| run_clear_prompt_gui, [], [prompt_gui, neg_prompt_gui] | |
| ) | |
| def run_set_random_seed(): | |
| return -1 | |
| set_random_seed.click( | |
| run_set_random_seed, [], seed_gui | |
| ) | |
| num_images_gui = gr.Slider( | |
| minimum=MINIMUM_IMAGE_NUMBER, | |
| maximum=MAXIMUM_IMAGE_NUMBER, | |
| step=1, | |
| value=1, | |
| label="Images" | |
| ) | |
| prompt_s_options = [ | |
| ("Classic format: (word:weight)", "Classic"), | |
| ("Compel format: (word)weight", "Compel"), | |
| ("Classic-original format: (word:weight)", "Classic-original"), | |
| ("Classic-no_norm format: (word:weight)", "Classic-no_norm"), | |
| ("Classic-ignore", "Classic-ignore"), | |
| ("None", "None"), | |
| ] | |
| 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("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=1.4, | |
| 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("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("From URL", open=False, visible=True): | |
| text_lora = gr.Textbox(label="URL", placeholder="http://...my_lora_url.safetensors", lines=1) | |
| button_lora = gr.Button("Get and update lists of LoRAs") | |
| button_lora.click( | |
| get_my_lora, | |
| [text_lora], | |
| [lora1_gui, lora2_gui, lora3_gui, lora4_gui, lora5_gui] | |
| ) | |
| with gr.Accordion("IP-Adapter", open=False, visible=True): ############## | |
| IP_MODELS = sorted(list(set(IP_ADAPTERS_SD + IP_ADAPTERS_SDXL))) | |
| MODE_IP_OPTIONS = ["original", "style", "layout", "style+layout"] | |
| with gr.Accordion("IP-Adapter 1", open=False, visible=True): | |
| image_ip1 = gr.Image(label="IP Image", type="filepath") | |
| mask_ip1 = gr.Image(label="IP Mask", type="filepath") | |
| model_ip1 = gr.Dropdown(value="plus_face", label="Model", choices=IP_MODELS) | |
| mode_ip1 = gr.Dropdown(value="original", label="Mode", choices=MODE_IP_OPTIONS) | |
| scale_ip1 = gr.Slider(minimum=0., maximum=2., step=0.01, value=0.7, label="Scale") | |
| with gr.Accordion("IP-Adapter 2", open=False, visible=True): | |
| image_ip2 = gr.Image(label="IP Image", type="filepath") | |
| mask_ip2 = gr.Image(label="IP Mask (optional)", type="filepath") | |
| model_ip2 = gr.Dropdown(value="base", label="Model", choices=IP_MODELS) | |
| mode_ip2 = gr.Dropdown(value="style", label="Mode", choices=MODE_IP_OPTIONS) | |
| scale_ip2 = gr.Slider(minimum=0., maximum=2., step=0.01, value=0.7, label="Scale") | |
| 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("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("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): | |
| image_previews_gui = gr.Checkbox(value=True, label="Image Previews") | |
| 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" | |
| ) | |
| retain_task_cache_gui = gr.Checkbox( | |
| value=False, | |
| label="Retain task model in cache" | |
| ) | |
| leave_progress_bar_gui = gr.Checkbox( | |
| value=True, | |
| label="Leave Progress Bar" | |
| ) | |
| disable_progress_bar_gui = gr.Checkbox( | |
| value=False, | |
| label="Disable Progress Bar" | |
| ) | |
| display_images_gui = gr.Checkbox( | |
| value=True, | |
| 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" | |
| ) | |
| # example and Help Section | |
| with gr.Accordion("Examples and help", open=False, visible=True): | |
| gr.Markdown( | |
| """### Help: | |
| - The current space runs on a ZERO GPU which is assigned for approximately 60 seconds; Therefore, \ | |
| if you submit expensive tasks, the operation may be canceled upon reaching the \ | |
| maximum allowed time with 'GPU TASK ABORTED'. | |
| - Distorted or strange images often result from high prompt weights, \ | |
| so it's best to use low weights and scales, and consider using Classic variants like 'Classic-original'. | |
| - For better results with Pony Diffusion, \ | |
| try using sampler DPM++ 1s or DPM2 with Compel or Classic prompt weights. | |
| """ | |
| ) | |
| gr.Markdown( | |
| """### The following examples perform specific tasks: | |
| 1. Generation with SDXL and upscale | |
| 2. Generation with SDXL | |
| 3. ControlNet Canny SDXL | |
| 4. Optical pattern (Optical illusion) SDXL | |
| 5. Convert an image to a coloring drawing | |
| 6. ControlNet OpenPose SD 1.5 | |
| - Different tasks can be performed, such as img2img or using the IP adapter, to preserve a person's appearance or a specific style based on an image. | |
| """ | |
| ) | |
| gr.Examples( | |
| examples=[ | |
| [ | |
| """ | |
| 1girl, loli, serious, cocky, sporty, basketball jersey, lakers, basketball, half body, | |
| (basketball court), sweaty, dribble, high detailed, sunny, day light, | |
| score_9, score_8_up, score_7_up, very aesthetic, | |
| layered, white hair, featuring soft waves and a slight outward curl at the ends, | |
| parted in the middle, (short hair), | |
| red glowing eyes, beautiful hazel red eyes, highly detailed eyes, thin eyebrows, | |
| detailed black eyebrows, | |
| long eyelashes, | |
| detailed kornea, | |
| fisheye, | |
| blush, | |
| parted lips, gorgeous lips, pink thin lips, | |
| detailed ear, human ears, human ear, highly detailed ears, highly detailed ear, detailed ears, | |
| perfect anatomy, | |
| five fingers, | |
| two hands, | |
| short girl, narrow body, detailed face, petite, | |
| medium boobs, | |
| armpits, (naval), | |
| """, | |
| """ | |
| (EasyNegative:1.05), easynegative, bad_prompt_version2, (poorly rendered), ugly, disfigured, | |
| cross eyed, cloned face, bad symmetry, bad anatomy, low quality, blurry, text, watermark, logo, | |
| signature, jpeg, artifacts, monochrome, paintings, oil, (hands:1.15), European Woman, woman, | |
| noise, dark skin, (3d), By bad artist -neg,bhands-neg, canvas frame, | |
| """, | |
| 1, | |
| 43, | |
| 7.5, | |
| True, | |
| -1, | |
| "None", | |
| 1.0, | |
| None, | |
| 1.0, | |
| None, | |
| 1.0, | |
| None, | |
| 1.0, | |
| None, | |
| 1.0, | |
| "DPM++ 2M Karras", | |
| 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 | |
| "canny ControlNet", | |
| "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, | |
| ], | |
| [ | |
| "cinematic scenery old city ruins", | |
| """ | |
| (worst quality, low quality, illustration, 3d, 2d, painting, cartoons, sketch), | |
| (illustration, 3d, 2d, painting, cartoons, sketch, blurry, film grain, noise), | |
| (low quality, worst quality:1.2) | |
| """, | |
| 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 | |
| "optical pattern ControlNet", | |
| "spiral_no_transparent.png", # 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 | |
| 0.05, # cn start | |
| 0.75, # cn end | |
| False, # ti | |
| "Classic", | |
| None, | |
| ], | |
| [ | |
| """ | |
| black and white, line art, coloring drawing, clean line art, black strokes, no background, | |
| white, black, free lines, black scribbles, on paper, | |
| A blend of comic book art and lineart full of black and white color, masterpiece, | |
| high-resolution, trending on Pixiv fan box, palette knife, brush strokes, two-dimensional, | |
| planar vector, T-shirt design, stickers, and T-shirt design, vector art, fantasy art, | |
| Adobe Illustrator, hand-painted, digital painting, low polygon, soft lighting, aerial view, | |
| isometric style, retro aesthetics, 8K resolution, black sketch lines, monochrome, invert color | |
| """, | |
| """ | |
| color, red, green, yellow, colored, duplicate, blurry, abstract, disfigured, deformed, | |
| animated, toy, figure, framed, 3d, bad art, poorly drawn, extra limbs, close up, b&w, | |
| weird colors, blurry, watermark, blur haze, 2 heads, long neck, watermark, elongated body, | |
| cropped image, out of frame, draft, deformed hands, twisted fingers, double image, | |
| malformed hands, multiple heads, extra limb, ugly, poorly drawn hands, missing limb, | |
| cut-off, over satured, grain, lowères, bad anatomy, poorly drawn face, mutation, mutated, | |
| floating limbs, disconnected limbs, out of focus, long body, disgusting, extra fingers, | |
| groos proportions, missing arms, mutated hands, cloned face, missing legs, ugly, tiling, | |
| poorly drawn hands, poorly drawn feet, poorly drawn face, out of frame, extra limbs, | |
| disfigured, deformed, body out of frame, blurry, bad anatomy, blurred, watermark, grainy, | |
| signature, cut off, draft, deformed, blurry, bad anatomy, disfigured, poorly drawn face, | |
| mutation, bluelish, blue | |
| """, | |
| 1, | |
| 20, | |
| 4., | |
| True, | |
| -1, | |
| "loras/Coloring_book_-_LineArt.safetensors", | |
| 1.0, | |
| None, | |
| 1.0, | |
| None, | |
| 1.0, | |
| None, | |
| 1.0, | |
| None, | |
| 1.0, | |
| "DPM++ 2M SDE Karras", | |
| 1024, | |
| 1024, | |
| "cagliostrolab/animagine-xl-3.1", | |
| None, # vae | |
| "lineart ControlNet", | |
| "color_image.png", # img conttol | |
| "Lineart", # preprocessor | |
| 512, # preproc resolution | |
| 896, # 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", | |
| 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 | |
| "openpose 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 | |
| 0.9, # 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.load_new_model, | |
| inputs=[ | |
| model_name_gui, | |
| vae_model_gui, | |
| task_gui | |
| ], | |
| outputs=[load_model_gui], | |
| queue=True, | |
| show_progress="minimal", | |
| ).success( | |
| 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, | |
| retain_task_cache_gui, | |
| image_ip1, | |
| mask_ip1, | |
| model_ip1, | |
| mode_ip1, | |
| scale_ip1, | |
| image_ip2, | |
| mask_ip2, | |
| model_ip2, | |
| mode_ip2, | |
| scale_ip2, | |
| ], | |
| outputs=[result_images, actual_task_info], | |
| queue=True, | |
| show_progress="minimal", | |
| ) | |
| app.queue() | |
| app.launch( | |
| show_error=True, | |
| debug=True, | |
| ) | |