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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 | |
| 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 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) | |
| ## BEGIN MOD | |
| from modutils import ( | |
| download_private_repo, | |
| get_model_id_list, | |
| list_uniq, | |
| get_tupled_embed_list, | |
| ) | |
| directory_user_loras = 'loras' | |
| os.makedirs(directory_user_loras, exist_ok=True) | |
| # - **Download SD 1.5 Models** | |
| #download_model = "https://huggingface.co/frankjoshua/toonyou_beta6/resolve/main/toonyou_beta6.safetensors" | |
| download_model = "" | |
| # - **Download VAEs** | |
| download_vae_list = [ | |
| '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', | |
| ] | |
| download_vae = ", ".join(download_vae_list) | |
| # - **Download LoRAs** | |
| download_lora_list = [] | |
| download_lora = ", ".join(download_lora_list) | |
| download_private_repo('John6666/loratest', directory_loras, True) | |
| download_private_repo('John6666/vaetest', directory_vaes, False) | |
| 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', | |
| 'KBlueLeaf/Kohaku-XL-Epsilon-rev2' | |
| 'Raelina/Rae-Diffusion-XL-V2', | |
| ] | |
| load_diffusers_format_model = list_uniq(get_model_id_list() + load_diffusers_format_model) | |
| ## END MOD | |
| 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") | |
| ## BEGIN MOD | |
| directory_embeds_sdxl = 'embedings_sdxl' | |
| os.makedirs(directory_embeds_sdxl, exist_ok=True) | |
| download_private_repo('John6666/embeddingstest', directory_embeds_sdxl, False) | |
| directory_embeds_postitive_sdxl = 'embedings_sdxl/positive' | |
| os.makedirs(directory_embeds_postitive_sdxl, exist_ok=True) | |
| download_private_repo('John6666/embeddingspositivetest', directory_embeds_postitive_sdxl, False) | |
| embed_sdxl_list = get_model_list(directory_embeds_sdxl) + get_model_list(directory_embeds_postitive_sdxl) | |
| def get_embed_list(pipeline_name): | |
| return get_tupled_embed_list(embed_sdxl_list if pipeline_name == "StableDiffusionXLPipeline" else embed_list) | |
| ## END MOD | |
| 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") | |
| ## BEGIN MOD | |
| from stablepy import logger | |
| logger.setLevel(logging.CRITICAL) | |
| from v2 import ( | |
| V2UI, | |
| parse_upsampling_output, | |
| V2_ALL_MODELS, | |
| ) | |
| from utils import ( | |
| gradio_copy_text, | |
| COPY_ACTION_JS, | |
| V2_ASPECT_RATIO_OPTIONS, | |
| V2_RATING_OPTIONS, | |
| V2_LENGTH_OPTIONS, | |
| V2_IDENTITY_OPTIONS | |
| ) | |
| from tagger import ( | |
| predict_tags_wd, | |
| convert_danbooru_to_e621_prompt, | |
| remove_specific_prompt, | |
| insert_recom_prompt, | |
| compose_prompt_to_copy, | |
| translate_prompt, | |
| ) | |
| from modutils import ( | |
| get_t2i_model_info, | |
| save_gallery_images, | |
| upload_file_lora, | |
| move_file_lora, | |
| set_lora_prompt, | |
| set_lora_trigger, | |
| apply_lora_prompt, | |
| set_textual_inversion_prompt, | |
| get_model_pipeline, | |
| set_optimization, | |
| set_sampler_settings, | |
| process_style_prompt, | |
| optimization_list, | |
| preset_styles, | |
| preset_quality, | |
| preset_sampler_setting, | |
| set_quick_presets, | |
| ) | |
| def description_ui(): | |
| gr.Markdown( | |
| """ | |
| ## Danbooru Tags Transformer V2 Demo with WD Tagger | |
| (Image =>) Prompt => Upsampled longer prompt | |
| - Mod of p1atdev's [Danbooru Tags Transformer V2 Demo](https://huggingface.co/spaces/p1atdev/danbooru-tags-transformer-v2) and [WD Tagger with 🤗 transformers](https://huggingface.co/spaces/p1atdev/wd-tagger-transformers). | |
| - Models: p1atdev's [wd-swinv2-tagger-v3-hf](https://huggingface.co/p1atdev/wd-swinv2-tagger-v3-hf), [dart-v2-moe-sft](https://huggingface.co/p1atdev/dart-v2-moe-sft) | |
| """ | |
| ) | |
| ## END MOD | |
| 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}" | |
| ## BEGIN MOD | |
| def generate_pipeline( | |
| ## END MOD | |
| 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 = [] | |
| params_ip_msk = [] | |
| params_ip_model = [] | |
| params_ip_mode = [] | |
| params_ip_scale = [] | |
| 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, | |
| ) | |
| ## BEGIN MOD | |
| # if textual_inversion and self.model.class_name == "StableDiffusionXLPipeline": | |
| # print("No Textual inversion for SDXL") | |
| ## END MOD | |
| 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, | |
| ## BEGIN MOD | |
| "textual_inversion": get_embed_list(self.model.class_name) if textual_inversion else [], | |
| ## END MOD | |
| "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() | |
| ## BEGIN MOD | |
| CSS =""" | |
| .contain { display: flex; flex-direction: column; } | |
| #component-0 { height: 100%; } | |
| #gallery { flex-grow: 1; } | |
| """ | |
| ## END MOD | |
| 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) | |
| ## BEGIN MOD | |
| with gr.Blocks(theme="NoCrypt/miku", elem_id="main", css=CSS) as app: | |
| gr.Markdown("# 🧩 DiffuseCraft Mod") | |
| gr.Markdown( | |
| f""" | |
| This space is a modification of [r3gm's DiffuseCraft](https://huggingface.co/spaces/r3gm/DiffuseCraft). | |
| """ | |
| ) | |
| with gr.Row(): | |
| with gr.Tab("Generation"): | |
| v2b = V2UI() | |
| with gr.Column(scale=2): | |
| with gr.Accordion("Model and Task", open=False): | |
| 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="votepurchase/animagine-xl-3.1", allow_custom_value=True) | |
| model_info_gui = gr.Markdown() | |
| with gr.Row(): | |
| quick_model_type_gui = gr.Radio(label="Model Type", choices=["None", "Animagine", "Pony"], value="None", interactive=True) | |
| quick_genre_gui = gr.Radio(label="Genre", choices=["Anime", "Photo"], value="Anime", interactive=True) | |
| quick_speed_gui = gr.Radio(label="Speed", choices=["Fast", "Standard", "Heavy"], value="Standard", interactive=True) | |
| quick_aspect_gui = gr.Radio(label="Aspect Ratio", choices=["1:1", "3:4"], value="3:4", interactive=True) | |
| quality_selector_gui = gr.Dropdown(label="Quality Tags Presets", interactive=True, choices=list(preset_quality.keys()), value="None") | |
| style_selector_gui = gr.Dropdown(label="Style Preset", interactive=True, choices=list(preset_styles.keys()), value="None") | |
| sampler_selector_gui = gr.Dropdown(label="Sampler Quick Settings", interactive=True, choices=list(preset_sampler_setting.keys()), value="None") | |
| optimization_gui = gr.Dropdown(label="Optimization for SDXL", choices=list(optimization_list.keys()), value="None", interactive=True) | |
| with gr.Accordion("Generate prompt from Image", open=False): | |
| input_image_gui = gr.Image(label="Input image", type="pil", sources=["upload", "clipboard"], height=256) | |
| with gr.Accordion(label="Advanced options", open=False): | |
| general_threshold_gui = gr.Slider(label="Threshold", minimum=0.0, maximum=1.0, value=0.3, step=0.01, interactive=True) | |
| character_threshold_gui = gr.Slider(label="Character threshold", minimum=0.0, maximum=1.0, value=0.8, step=0.01, interactive=True) | |
| tag_type_gui = gr.Radio(label="Convert tags to", info="danbooru for Animagine, e621 for Pony.", choices=["danbooru", "e621"], value="danbooru") | |
| recom_prompt_gui = gr.Radio(label="Insert reccomended prompt", choices=["None", "Animagine", "Pony"], value="None", interactive=True) | |
| keep_tags_gui = gr.Radio(label="Remove tags leaving only the following", choices=["body", "dress", "all"], value="all") | |
| image_algorithms = gr.CheckboxGroup(["Use WD Tagger", "Use Florence-2-SD3-Long-Captioner"], label="Algorithms", value=["Use WD Tagger"], visible=False) | |
| generate_from_image_btn_gui = gr.Button(value="GENERATE TAGS FROM IMAGE", size="lg", variant="primary") | |
| with gr.Group(): | |
| prompt_gui = gr.Textbox(lines=6, placeholder="1girl, solo, ...", label="Prompt", show_copy_button=True) | |
| with gr.Accordion("Negative prompt, etc.", open=False): | |
| neg_prompt_gui = gr.Textbox(lines=3, placeholder="lowres, (bad), ...", label="Negative prompt", show_copy_button=True) | |
| translate_prompt_button = gr.Button(value="Translate prompt to English", size="sm", variant="secondary") | |
| insert_prompt_gui = gr.Radio(label="Insert reccomended positive / negative prompt", choices=["None", "Animagine", "Pony"], value="None", interactive=True, scale=2) | |
| prompt_type_gui = gr.Radio(label="Convert tags to", choices=["danbooru", "e621"], value="e621", visible=False) | |
| prompt_type_button = gr.Button(value="Convert prompt to Pony e621 style", size="sm", variant="secondary") | |
| with gr.Row(): | |
| character_dbt = gr.Textbox(lines=1, placeholder="kafuu chino, ...", label="Character names") | |
| series_dbt = gr.Textbox(lines=1, placeholder="Is the order a rabbit?, ...", label="Series names") | |
| generate_db_random_button = gr.Button(value="Generate random prompt from character", size="sm", variant="secondary") | |
| model_name_dbt = gr.Dropdown(label="Model", choices=list(V2_ALL_MODELS.keys()), value=list(V2_ALL_MODELS.keys())[0], visible=False) | |
| aspect_ratio_dbt = gr.Radio(label="Aspect ratio", choices=list(V2_ASPECT_RATIO_OPTIONS), value="square", visible=False) | |
| length_dbt = gr.Radio(label="Length", choices=list(V2_LENGTH_OPTIONS), value="very_long", visible=False) | |
| identity_dbt = gr.Radio(label="Keep identity", choices=list(V2_IDENTITY_OPTIONS), value="lax", visible=False) | |
| ban_tags_dbt = gr.Textbox(label="Ban tags", placeholder="alternate costumen, ...", value="futanari, censored, furry, furrification", visible=False) | |
| elapsed_time_dbt = gr.Markdown(label="Elapsed time", value="", visible=False) | |
| copy_button_dbt = gr.Button(value="Copy to clipboard", visible=False) | |
| rating_dbt = gr.Radio(label="Rating", choices=list(V2_RATING_OPTIONS), value="explicit") | |
| with gr.Row(): | |
| set_params_gui = gr.Button(value="↙️") | |
| clear_prompt_gui = gr.Button(value="🗑️") | |
| set_random_seed = gr.Button(value="🎲") | |
| generate_button = gr.Button(value="GENERATE IMAGE", size="lg", 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, | |
| show_share_button=False, | |
| show_download_button=True, | |
| selected_index=50, | |
| format="png", | |
| ) | |
| result_images_files = gr.Files(interactive=False, visible=False) | |
| actual_task_info = gr.HTML() | |
| with gr.Accordion("Generation settings", open=False, visible=True): | |
| steps_gui = gr.Slider(minimum=1, maximum=100, step=1, value=28, label="Steps") | |
| cfg_gui = gr.Slider(minimum=0, maximum=30, step=0.5, value=7.0, 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") | |
| with gr.Row(): | |
| clip_skip_gui = gr.Checkbox(value=False, label="Layer 2 Clip Skip") | |
| free_u_gui = gr.Checkbox(value=False, label="FreeU") | |
| seed_gui = gr.Number(minimum=-1, maximum=9999999999, value=-1, label="Seed") | |
| 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=1, maximum=4, step=1, value=1, label="Images") | |
| prompt_s_options = [ | |
| ("Compel format: (word)weight", "Compel"), | |
| ("Classic format: (word:weight)", "Classic"), | |
| ("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[1][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=None) | |
| upscaler_increases_size_gui = gr.Slider(minimum=1.1, maximum=6., step=0.1, value=1.0, 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") | |
| lora1_trigger_gui = gr.Textbox(label="Lora1 prompts", value="", show_copy_button=True, interactive=False, visible=False) | |
| 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") | |
| lora2_trigger_gui = gr.Textbox(label="Lora2 prompts", value="", show_copy_button=True, interactive=False, visible=False) | |
| 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") | |
| lora3_trigger_gui = gr.Textbox(label="Lora3 prompts", value="", show_copy_button=True, interactive=False, visible=False) | |
| 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") | |
| lora4_trigger_gui = gr.Textbox(label="Lora4 prompts", value="", show_copy_button=True, interactive=False, visible=False) | |
| 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") | |
| lora5_trigger_gui = gr.Textbox(label="Lora5 prompts", value="", show_copy_button=True, interactive=False, visible=False) | |
| with gr.Accordion("From URL", open=True, 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("From Local", open=True, visible=True): | |
| file_output_lora = gr.File(file_types=['.ckpt', '.pt', '.pth', '.safetensors', '.bin'], file_count="multiple", visible=False) | |
| upload_button_lora = gr.UploadButton(label="Upload LoRA from your disk (very slow)", file_types=['.ckpt' , '.pt', '.pth', '.safetensors', '.bin'], file_count="multiple") | |
| upload_button_lora.upload(upload_file_lora, upload_button_lora, file_output_lora).success( | |
| move_file_lora, | |
| [file_output_lora, gr.Textbox(value=directory_loras, visible=False), gr.Textbox(value=directory_user_loras, visible=False)], | |
| [lora1_gui, lora2_gui, lora3_gui, lora4_gui, lora5_gui] | |
| ) | |
| ## END MOD | |
| 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]) | |
| ## BEGIN MOD | |
| with gr.Accordion("Textual inversion", open=False, visible=True): | |
| active_textual_inversion_gui = gr.Checkbox(value=False, label="Active Textual Inversion in prompt") | |
| use_textual_inversion_gui = gr.CheckboxGroup(choices=get_embed_list(get_model_pipeline(model_name_gui.value)) if active_textual_inversion_gui.value else [], value=None, label="Use Textual Invertion in prompt") | |
| def update_textual_inversion_gui(active_textual_inversion_gui, model_name_gui): | |
| return gr.update(choices=get_embed_list(get_model_pipeline(str(model_name_gui))) if active_textual_inversion_gui else []) | |
| active_textual_inversion_gui.change(update_textual_inversion_gui, [active_textual_inversion_gui, model_name_gui], [use_textual_inversion_gui]) | |
| model_name_gui.change(update_textual_inversion_gui, [active_textual_inversion_gui, model_name_gui], [use_textual_inversion_gui]) | |
| ## END MOD | |
| 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") | |
| ## BEGIN MOD | |
| with gr.Accordion("Examples and help", open=True, 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, | |
| "votepurchase/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", | |
| None, | |
| ], | |
| [ | |
| "solo, princess Zelda OOT, score_9, score_8_up, score_8, medium breasts, cute, eyelashes, cute small face, long hair, crown braid, hairclip, pointy ears, soft curvy body, 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, | |
| "Euler a", | |
| 1024, | |
| 1024, | |
| "votepurchase/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", | |
| None, | |
| ], | |
| [ | |
| "1girl, oomuro sakurako, yuru yuri, official art, school uniform, anime artwork, anime style, key visual, vibrant, studio anime, highly detailed, masterpiece, best quality, very aesthetic, absurdres", | |
| "photo, deformed, black and white, realism, disfigured, low contrast, 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, | |
| 40, | |
| 7.0, | |
| True, | |
| -1, | |
| None, | |
| 1.0, | |
| None, | |
| 1.0, | |
| None, | |
| 1.0, | |
| None, | |
| 1.0, | |
| None, | |
| 1.0, | |
| "Euler", | |
| 1024, | |
| 1024, | |
| "Raelina/Rae-Diffusion-XL-V2", | |
| "vaes/sdxl.vae.safetensors", # 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, | |
| ], | |
| [ | |
| "yoshida yuuko, machikado mazoku, 1girl, solo, demon horns,horns, school uniform, long hair, open mouth, skirt, demon girl, ahoge, shiny, shiny hair, anime artwork", | |
| "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, | |
| 50, | |
| 7., | |
| True, | |
| -1, | |
| None, | |
| 1.0, | |
| None, | |
| 1.0, | |
| None, | |
| 1.0, | |
| None, | |
| 1.0, | |
| None, | |
| 1.0, | |
| "Euler a", | |
| 1024, | |
| 1024, | |
| "votepurchase/animagine-xl-3.1", | |
| None, # vae | |
| "img2img", | |
| "color_image.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., # cn start | |
| 1., # cn end | |
| False, # ti | |
| "Classic", | |
| None, | |
| ], | |
| ], | |
| 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, | |
| elem_id="examples", | |
| ) | |
| ## END MOD | |
| 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]) | |
| ## BEGIN MOD | |
| model_name_gui.change(get_t2i_model_info, [model_name_gui], [model_info_gui]) | |
| quick_model_type_gui.change( | |
| set_quick_presets, | |
| [quick_genre_gui, quick_model_type_gui, quick_speed_gui, quick_aspect_gui], | |
| [quality_selector_gui, style_selector_gui, sampler_selector_gui, optimization_gui], | |
| ) | |
| quick_genre_gui.change( | |
| set_quick_presets, | |
| [quick_genre_gui, quick_model_type_gui, quick_speed_gui, quick_aspect_gui], | |
| [quality_selector_gui, style_selector_gui, sampler_selector_gui, optimization_gui], | |
| ) | |
| quick_speed_gui.change( | |
| set_quick_presets, | |
| [quick_genre_gui, quick_model_type_gui, quick_speed_gui, quick_aspect_gui], | |
| [quality_selector_gui, style_selector_gui, sampler_selector_gui, optimization_gui], | |
| ) | |
| quick_aspect_gui.change( | |
| set_quick_presets, | |
| [quick_genre_gui, quick_model_type_gui, quick_speed_gui, quick_aspect_gui], | |
| [quality_selector_gui, style_selector_gui, sampler_selector_gui, optimization_gui], | |
| ) | |
| quality_selector_gui.change( | |
| process_style_prompt, | |
| inputs=[prompt_gui, neg_prompt_gui, style_selector_gui, quality_selector_gui, insert_prompt_gui], | |
| outputs=[prompt_gui, neg_prompt_gui], | |
| ) | |
| style_selector_gui.change( | |
| process_style_prompt, | |
| inputs=[prompt_gui, neg_prompt_gui, style_selector_gui, quality_selector_gui, insert_prompt_gui], | |
| outputs=[prompt_gui, neg_prompt_gui], | |
| ) | |
| sampler_selector_gui.change(set_sampler_settings, [sampler_selector_gui], [sampler_gui, steps_gui, cfg_gui, clip_skip_gui, img_width_gui, img_height_gui, optimization_gui]) | |
| optimization_gui.change(set_optimization, [optimization_gui, steps_gui, cfg_gui, sampler_gui, clip_skip_gui, lora1_gui, lora_scale_1_gui], [steps_gui, cfg_gui, sampler_gui, clip_skip_gui, lora1_gui, lora_scale_1_gui]) | |
| lora1_gui.change(set_lora_prompt, [prompt_gui, prompt_syntax_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], [prompt_gui])\ | |
| .then(set_lora_trigger, [lora1_gui], [lora1_trigger_gui]) | |
| lora2_gui.change(set_lora_prompt, [prompt_gui, prompt_syntax_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], [prompt_gui])\ | |
| .then(set_lora_trigger, [lora2_gui], [lora2_trigger_gui]) | |
| lora3_gui.change(set_lora_prompt, [prompt_gui, prompt_syntax_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], [prompt_gui])\ | |
| .then(set_lora_trigger, [lora3_gui], [lora3_trigger_gui]) | |
| lora4_gui.change(set_lora_prompt, [prompt_gui, prompt_syntax_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], [prompt_gui])\ | |
| .then(set_lora_trigger, [lora4_gui], [lora4_trigger_gui]) | |
| lora5_gui.change(set_lora_prompt, [prompt_gui, prompt_syntax_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], [prompt_gui])\ | |
| .then(set_lora_trigger, [lora5_gui], [lora5_trigger_gui]) | |
| lora_scale_1_gui.change(set_lora_prompt, [prompt_gui, prompt_syntax_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], [prompt_gui]) | |
| lora_scale_2_gui.change(set_lora_prompt, [prompt_gui, prompt_syntax_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], [prompt_gui]) | |
| lora_scale_3_gui.change(set_lora_prompt, [prompt_gui, prompt_syntax_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], [prompt_gui]) | |
| lora_scale_4_gui.change(set_lora_prompt, [prompt_gui, prompt_syntax_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], [prompt_gui]) | |
| lora_scale_5_gui.change(set_lora_prompt, [prompt_gui, prompt_syntax_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], [prompt_gui]) | |
| prompt_syntax_gui.change(set_lora_prompt, [prompt_gui, prompt_syntax_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], [prompt_gui]) | |
| lora1_trigger_gui.select(apply_lora_prompt, [prompt_gui, lora1_trigger_gui], [prompt_gui]) | |
| lora2_trigger_gui.select(apply_lora_prompt, [prompt_gui, lora2_trigger_gui], [prompt_gui]) | |
| lora3_trigger_gui.select(apply_lora_prompt, [prompt_gui, lora3_trigger_gui], [prompt_gui]) | |
| lora4_trigger_gui.select(apply_lora_prompt, [prompt_gui, lora4_trigger_gui], [prompt_gui]) | |
| lora5_trigger_gui.select(apply_lora_prompt, [prompt_gui, lora5_trigger_gui], [prompt_gui]) | |
| use_textual_inversion_gui.change(set_textual_inversion_prompt, [use_textual_inversion_gui, prompt_gui, neg_prompt_gui, prompt_syntax_gui], [prompt_gui, neg_prompt_gui]) | |
| generate_from_image_btn_gui.click( | |
| predict_tags_wd, | |
| inputs=[input_image_gui, prompt_gui, image_algorithms, general_threshold_gui, character_threshold_gui], | |
| outputs=[ | |
| series_dbt, | |
| character_dbt, | |
| prompt_gui, | |
| copy_button_dbt, | |
| ], | |
| ).then( | |
| compose_prompt_to_copy, inputs=[character_dbt, series_dbt, prompt_gui], outputs=[prompt_gui] | |
| ).then( | |
| remove_specific_prompt, inputs=[prompt_gui, keep_tags_gui], outputs=[prompt_gui], | |
| ).then( | |
| convert_danbooru_to_e621_prompt, inputs=[prompt_gui, tag_type_gui], outputs=[prompt_gui], | |
| ).then( | |
| insert_recom_prompt, inputs=[prompt_gui, neg_prompt_gui, recom_prompt_gui], outputs=[prompt_gui, neg_prompt_gui], | |
| ) | |
| v2b.input_components = [ | |
| model_name_dbt, | |
| series_dbt, | |
| character_dbt, | |
| prompt_gui, | |
| rating_dbt, | |
| aspect_ratio_dbt, | |
| length_dbt, | |
| identity_dbt, | |
| ban_tags_dbt, | |
| ] | |
| insert_prompt_gui.change( | |
| process_style_prompt, | |
| inputs=[prompt_gui, neg_prompt_gui, style_selector_gui, quality_selector_gui, insert_prompt_gui], | |
| outputs=[prompt_gui, neg_prompt_gui], | |
| ) | |
| prompt_type_button.click( | |
| convert_danbooru_to_e621_prompt, | |
| inputs=[prompt_gui, prompt_type_gui], | |
| outputs=[prompt_gui], | |
| ) | |
| generate_db_random_button.click( | |
| parse_upsampling_output(v2b.on_generate), | |
| inputs=[ | |
| *v2b.input_components, | |
| ], | |
| outputs=[prompt_gui, elapsed_time_dbt, copy_button_dbt, copy_button_dbt], | |
| ) | |
| translate_prompt_button.click(translate_prompt, inputs=[prompt_gui], outputs=[prompt_gui]) | |
| translate_prompt_button.click(translate_prompt, inputs=[character_dbt], outputs=[character_dbt]) | |
| translate_prompt_button.click(translate_prompt, inputs=[series_dbt], outputs=[series_dbt]) | |
| 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", | |
| ).success(save_gallery_images, [result_images], [result_images, result_images_files, result_images_files]) | |
| with gr.Tab("Danbooru Tags Transformer with WD Tagger", render=True): | |
| v2 = V2UI() | |
| with gr.Column(scale=2): | |
| with gr.Group(): | |
| input_image = gr.Image(label="Input image", type="pil", sources=["upload", "clipboard"], height=256) | |
| with gr.Accordion(label="Advanced options", open=False): | |
| general_threshold = gr.Slider(label="Threshold", minimum=0.0, maximum=1.0, value=0.3, step=0.01, interactive=True) | |
| character_threshold = gr.Slider(label="Character threshold", minimum=0.0, maximum=1.0, value=0.8, step=0.01, interactive=True) | |
| input_tag_type = gr.Radio(label="Convert tags to", info="danbooru for Animagine, e621 for Pony.", choices=["danbooru", "e621"], value="danbooru") | |
| recom_prompt = gr.Radio(label="Insert reccomended prompt", choices=["None", "Animagine", "Pony"], value="None", interactive=True) | |
| image_algorithms = gr.CheckboxGroup(["Use WD Tagger", "Use Florence-2-SD3-Long-Captioner"], label="Algorithms", value=["Use WD Tagger"], visible=False) | |
| keep_tags = gr.Radio(label="Remove tags leaving only the following", choices=["body", "dress", "all"], value="all") | |
| generate_from_image_btn = gr.Button(value="GENERATE TAGS FROM IMAGE", size="lg", variant="primary") | |
| with gr.Group(): | |
| input_character = gr.Textbox(label="Character tags", placeholder="hatsune miku") | |
| input_copyright = gr.Textbox(label="Copyright tags", placeholder="vocaloid") | |
| input_general = gr.TextArea(label="General tags", lines=4, placeholder="1girl, ...", value="") | |
| input_tags_to_copy = gr.Textbox(value="", visible=False) | |
| copy_input_btn = gr.Button(value="Copy to clipboard", size="sm", interactive=False) | |
| translate_input_prompt_button = gr.Button(value="Translate prompt to English", size="sm", variant="secondary") | |
| tag_type = gr.Radio(label="Output tag conversion", info="danbooru for Animagine, e621 for Pony.", choices=["danbooru", "e621"], value="e621", visible=False) | |
| input_rating = gr.Radio(label="Rating", choices=list(V2_RATING_OPTIONS), value="explicit") | |
| with gr.Accordion(label="Advanced options", open=False): | |
| input_aspect_ratio = gr.Radio(label="Aspect ratio", info="The aspect ratio of the image.", choices=list(V2_ASPECT_RATIO_OPTIONS), value="square") | |
| input_length = gr.Radio(label="Length", info="The total length of the tags.", choices=list(V2_LENGTH_OPTIONS), value="very_long") | |
| input_identity = gr.Radio(label="Keep identity", info="How strictly to keep the identity of the character or subject. If you specify the detail of subject in the prompt, you should choose `strict`. Otherwise, choose `none` or `lax`. `none` is very creative but sometimes ignores the input prompt.", choices=list(V2_IDENTITY_OPTIONS), value="lax") | |
| input_ban_tags = gr.Textbox(label="Ban tags", info="Tags to ban from the output.", placeholder="alternate costumen, ...", value="censored") | |
| model_name = gr.Dropdown(label="Model", choices=list(V2_ALL_MODELS.keys()), value=list(V2_ALL_MODELS.keys())[0]) | |
| dummy_np = gr.Textbox(label="Negative prompt", value="", visible=False) | |
| recom_animagine = gr.Textbox(label="Animagine reccomended prompt", value="Animagine", visible=False) | |
| recom_pony = gr.Textbox(label="Pony reccomended prompt", value="Pony", visible=False) | |
| generate_btn = gr.Button(value="GENERATE TAGS", size="lg", variant="primary") | |
| with gr.Group(): | |
| output_text = gr.TextArea(label="Output tags", interactive=False, show_copy_button=True) | |
| copy_btn = gr.Button(value="Copy to clipboard", size="sm", interactive=False) | |
| elapsed_time_md = gr.Markdown(label="Elapsed time", value="", visible=False) | |
| with gr.Group(): | |
| output_text_pony = gr.TextArea(label="Output tags (Pony e621 style)", interactive=False, show_copy_button=True) | |
| copy_btn_pony = gr.Button(value="Copy to clipboard", size="sm", interactive=False) | |
| description_ui() | |
| v2.input_components = [ | |
| model_name, | |
| input_copyright, | |
| input_character, | |
| input_general, | |
| input_rating, | |
| input_aspect_ratio, | |
| input_length, | |
| input_identity, | |
| input_ban_tags, | |
| ] | |
| translate_input_prompt_button.click(translate_prompt, inputs=[input_general], outputs=[input_general]) | |
| translate_input_prompt_button.click(translate_prompt, inputs=[input_character], outputs=[input_character]) | |
| translate_input_prompt_button.click(translate_prompt, inputs=[input_copyright], outputs=[input_copyright]) | |
| generate_from_image_btn.click( | |
| predict_tags_wd, | |
| inputs=[input_image, input_general, image_algorithms, general_threshold, character_threshold], | |
| outputs=[ | |
| input_copyright, | |
| input_character, | |
| input_general, | |
| copy_input_btn, | |
| ], | |
| ).then( | |
| remove_specific_prompt, inputs=[input_general, keep_tags], outputs=[input_general], | |
| ).then( | |
| convert_danbooru_to_e621_prompt, inputs=[input_general, input_tag_type], outputs=[input_general], | |
| ).then( | |
| insert_recom_prompt, inputs=[input_general, dummy_np, recom_prompt], outputs=[input_general, dummy_np], | |
| ) | |
| copy_input_btn.click(compose_prompt_to_copy, inputs=[input_character, input_copyright, input_general], outputs=[input_tags_to_copy]).then( | |
| gradio_copy_text, inputs=[input_tags_to_copy], js=COPY_ACTION_JS, | |
| ) | |
| generate_btn.click( | |
| parse_upsampling_output(v2.on_generate), | |
| inputs=[ | |
| *v2.input_components, | |
| ], | |
| outputs=[output_text, elapsed_time_md, copy_btn, copy_btn_pony], | |
| ).then( | |
| convert_danbooru_to_e621_prompt, inputs=[output_text, tag_type], outputs=[output_text_pony], | |
| ).then( | |
| insert_recom_prompt, inputs=[output_text, dummy_np, recom_animagine], outputs=[output_text, dummy_np], | |
| ).then( | |
| insert_recom_prompt, inputs=[output_text_pony, dummy_np, recom_pony], outputs=[output_text_pony, dummy_np], | |
| ) | |
| copy_btn.click(gradio_copy_text, inputs=[output_text], js=COPY_ACTION_JS) | |
| copy_btn_pony.click(gradio_copy_text, inputs=[output_text_pony], js=COPY_ACTION_JS) | |
| gr.DuplicateButton(value="Duplicate Space for private use (This demo does not work on CPU. Requires Space with GPU available.)", elem_id="duplicate-button", visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1") | |
| app.queue() | |
| app.launch( | |
| show_error=False, | |
| debug=False, | |
| ) | |
| ## END MOD | |