from PIL import Image import torch import gradio as gr import spaces # ZERO GPU from transformers import ( AutoImageProcessor, AutoModelForImageClassification, ) WD_MODEL_NAMES = ["p1atdev/wd-swinv2-tagger-v3-hf"] WD_MODEL_NAME = WD_MODEL_NAMES[0] wd_model = AutoModelForImageClassification.from_pretrained(WD_MODEL_NAME, trust_remote_code=True) wd_model.to("cuda" if torch.cuda.is_available() else "cpu") wd_processor = AutoImageProcessor.from_pretrained(WD_MODEL_NAME, trust_remote_code=True) def _people_tag(noun: str, minimum: int = 1, maximum: int = 5): return ( [f"1{noun}"] + [f"{num}{noun}s" for num in range(minimum + 1, maximum + 1)] + [f"{maximum+1}+{noun}s"] ) PEOPLE_TAGS = ( _people_tag("girl") + _people_tag("boy") + _people_tag("other") + ["no humans"] ) RATING_MAP = { "general": "safe", "sensitive": "sensitive", "questionable": "nsfw", "explicit": "explicit, nsfw", } DANBOORU_TO_E621_RATING_MAP = { "safe": "rating_safe", "sensitive": "rating_safe", "nsfw": "rating_explicit", "explicit, nsfw": "rating_explicit", "explicit": "rating_explicit", "rating:safe": "rating_safe", "rating:general": "rating_safe", "rating:sensitive": "rating_safe", "rating:questionable, nsfw": "rating_explicit", "rating:explicit, nsfw": "rating_explicit", } def to_list(s): return [x.strip() for x in s.split(",") if not s == ""] def list_sub(a, b): return [e for e in a if e not in b] def list_uniq(l): return sorted(set(l), key=l.index) def load_dict_from_csv(filename): with open(filename, 'r', encoding="utf-8") as f: lines = f.readlines() dict = {} for line in lines: parts = line.strip().split(',') dict[parts[0]] = parts[1] return dict anime_series_dict = load_dict_from_csv('character_series_dict.csv') def character_list_to_series_list(character_list): output_series_tag = [] series_tag = "" series_dict = anime_series_dict for tag in character_list: series_tag = series_dict.get(tag, "") if tag.endswith(")"): tags = tag.split("(") character_tag = "(".join(tags[:-1]) if character_tag.endswith(" "): character_tag = character_tag[:-1] series_tag = tags[-1].replace(")", "") if series_tag: output_series_tag.append(series_tag) return output_series_tag def select_random_character(series: str, character: str): from random import randrange character_list = list(anime_series_dict.keys()) character = character_list[randrange(len(character_list) - 1)] series = anime_series_dict.get(character.split(",")[0].strip(), "") return series, character def danbooru_to_e621(dtag, e621_dict): def d_to_e(match, e621_dict): dtag = match.group(0) etag = e621_dict.get(dtag.strip().replace("_", " "), "") if etag: return etag else: return dtag import re tag = re.sub(r'[\w ]+', lambda wrapper: d_to_e(wrapper, e621_dict), dtag, 2) return tag danbooru_to_e621_dict = load_dict_from_csv('danbooru_e621.csv') def convert_danbooru_to_e621_prompt(input_prompt: str = "", prompt_type: str = "danbooru"): if prompt_type == "danbooru": return input_prompt tags = input_prompt.split(",") if input_prompt else [] people_tags: list[str] = [] other_tags: list[str] = [] rating_tags: list[str] = [] e621_dict = danbooru_to_e621_dict for tag in tags: tag = tag.strip().replace("_", " ") tag = danbooru_to_e621(tag, e621_dict) if tag in PEOPLE_TAGS: people_tags.append(tag) elif tag in DANBOORU_TO_E621_RATING_MAP.keys(): rating_tags.append(DANBOORU_TO_E621_RATING_MAP.get(tag.replace(" ",""), "")) else: other_tags.append(tag) rating_tags = sorted(set(rating_tags), key=rating_tags.index) rating_tags = [rating_tags[0]] if rating_tags else [] rating_tags = ["explicit, nsfw"] if rating_tags and rating_tags[0] == "explicit" else rating_tags output_prompt = ", ".join(people_tags + other_tags + rating_tags) return output_prompt def translate_prompt(prompt: str = ""): def translate_to_english(prompt): import httpcore setattr(httpcore, 'SyncHTTPTransport', 'AsyncHTTPProxy') from googletrans import Translator translator = Translator() try: translated_prompt = translator.translate(prompt, src='auto', dest='en').text return translated_prompt except Exception as e: return prompt def is_japanese(s): import unicodedata for ch in s: name = unicodedata.name(ch, "") if "CJK UNIFIED" in name or "HIRAGANA" in name or "KATAKANA" in name: return True return False def to_list(s): return [x.strip() for x in s.split(",")] prompts = to_list(prompt) outputs = [] for p in prompts: p = translate_to_english(p) if is_japanese(p) else p outputs.append(p) return ", ".join(outputs) def translate_prompt_to_ja(prompt: str = ""): def translate_to_japanese(prompt): import httpcore setattr(httpcore, 'SyncHTTPTransport', 'AsyncHTTPProxy') from googletrans import Translator translator = Translator() try: translated_prompt = translator.translate(prompt, src='en', dest='ja').text return translated_prompt except Exception as e: return prompt def is_japanese(s): import unicodedata for ch in s: name = unicodedata.name(ch, "") if "CJK UNIFIED" in name or "HIRAGANA" in name or "KATAKANA" in name: return True return False def to_list(s): return [x.strip() for x in s.split(",")] prompts = to_list(prompt) outputs = [] for p in prompts: p = translate_to_japanese(p) if not is_japanese(p) else p outputs.append(p) return ", ".join(outputs) def tags_to_ja(itag, dict): def t_to_j(match, dict): tag = match.group(0) ja = dict.get(tag.strip().replace("_", " "), "") if ja: return ja else: return tag import re tag = re.sub(r'[\w ]+', lambda wrapper: t_to_j(wrapper, dict), itag, 2) return tag def convert_tags_to_ja(input_prompt: str = ""): tags = input_prompt.split(",") if input_prompt else [] out_tags = [] tags_to_ja_dict = load_dict_from_csv('all_tags_ja_ext.csv') dict = tags_to_ja_dict for tag in tags: tag = tag.strip().replace("_", " ") tag = tags_to_ja(tag, dict) out_tags.append(tag) return ", ".join(out_tags) enable_auto_recom_prompt = True animagine_ps = to_list("anime artwork, anime style, studio anime, highly detailed, masterpiece, best quality, very aesthetic, absurdres") animagine_nps = to_list("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]") pony_ps = to_list("source_anime, score_9, score_8_up, score_7_up, masterpiece, best quality, very aesthetic, absurdres") pony_nps = to_list("source_pony, source_furry, source_cartoon, score_6, score_5, score_4, busty, ugly face, mutated hands, low res, blurry face, black and white, the simpsons, overwatch, apex legends") other_ps = to_list("anime artwork, anime style, studio anime, highly detailed, cinematic photo, 35mm photograph, film, bokeh, professional, 4k, highly detailed") other_nps = to_list("photo, deformed, black and white, realism, disfigured, low contrast, drawing, painting, crayon, sketch, graphite, impressionist, noisy, blurry, soft, deformed, ugly") default_ps = to_list("score_9, score_8_up, score_7_up, highly detailed, masterpiece, best quality, very aesthetic, absurdres") default_nps = to_list("score_6, score_5, score_4, 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]") def insert_recom_prompt(prompt: str = "", neg_prompt: str = "", type: str = "None"): global enable_auto_recom_prompt prompts = to_list(prompt) neg_prompts = to_list(neg_prompt) prompts = list_sub(prompts, animagine_ps + pony_ps) neg_prompts = list_sub(neg_prompts, animagine_nps + pony_nps) last_empty_p = [""] if not prompts and type != "None" else [] last_empty_np = [""] if not neg_prompts and type != "None" else [] if type == "Auto": enable_auto_recom_prompt = True else: enable_auto_recom_prompt = False if type == "Animagine": prompts = prompts + animagine_ps neg_prompts = neg_prompts + animagine_nps elif type == "Pony": prompts = prompts + pony_ps neg_prompts = neg_prompts + pony_nps prompt = ", ".join(list_uniq(prompts) + last_empty_p) neg_prompt = ", ".join(list_uniq(neg_prompts) + last_empty_np) return prompt, neg_prompt def load_model_prompt_dict(): import json dict = {} try: with open('model_dict.json', encoding='utf-8') as f: dict = json.load(f) except Exception: pass return dict model_prompt_dict = load_model_prompt_dict() def insert_model_recom_prompt(prompt: str = "", neg_prompt: str = "", model_name: str = "None"): if not model_name or not enable_auto_recom_prompt: return prompt, neg_prompt prompts = to_list(prompt) neg_prompts = to_list(neg_prompt) prompts = list_sub(prompts, animagine_ps + pony_ps + other_ps) neg_prompts = list_sub(neg_prompts, animagine_nps + pony_nps + other_nps) last_empty_p = [""] if not prompts and type != "None" else [] last_empty_np = [""] if not neg_prompts and type != "None" else [] ps = [] nps = [] if model_name in model_prompt_dict.keys(): ps = to_list(model_prompt_dict[model_name]["prompt"]) nps = to_list(model_prompt_dict[model_name]["negative_prompt"]) else: ps = default_ps nps = default_nps prompts = prompts + ps neg_prompts = neg_prompts + nps prompt = ", ".join(list_uniq(prompts) + last_empty_p) neg_prompt = ", ".join(list_uniq(neg_prompts) + last_empty_np) return prompt, neg_prompt tag_group_dict = load_dict_from_csv('tag_group.csv') def remove_specific_prompt(input_prompt: str = "", keep_tags: str = "all"): def is_dressed(tag): import re p = re.compile(r'dress|cloth|uniform|costume|vest|sweater|coat|shirt|jacket|blazer|apron|leotard|hood|sleeve|skirt|shorts|pant|loafer|ribbon|necktie|bow|collar|glove|sock|shoe|boots|wear|emblem') return p.search(tag) def is_background(tag): import re p = re.compile(r'background|outline|light|sky|build|day|screen|tree|city') return p.search(tag) un_tags = ['solo'] group_list = ['groups', 'body_parts', 'attire', 'posture', 'objects', 'creatures', 'locations', 'disambiguation_pages', 'commonly_misused_tags', 'phrases', 'verbs_and_gerunds', 'subjective', 'nudity', 'sex_objects', 'sex', 'sex_acts', 'image_composition', 'artistic_license', 'text', 'year_tags', 'metatags'] keep_group_dict = { "body": ['groups', 'body_parts'], "dress": ['groups', 'body_parts', 'attire'], "all": group_list, } def is_necessary(tag, keep_tags, group_dict): if keep_tags == "all": return True elif tag in un_tags or group_dict.get(tag, "") in explicit_group: return False elif keep_tags == "body" and is_dressed(tag): return False elif is_background(tag): return False else: return True if keep_tags == "all": return input_prompt keep_group = keep_group_dict.get(keep_tags, keep_group_dict["body"]) explicit_group = list(set(group_list) ^ set(keep_group)) tags = input_prompt.split(",") if input_prompt else [] people_tags: list[str] = [] other_tags: list[str] = [] group_dict = tag_group_dict for tag in tags: tag = tag.strip().replace("_", " ") if tag in PEOPLE_TAGS: people_tags.append(tag) elif is_necessary(tag, keep_tags, group_dict): other_tags.append(tag) output_prompt = ", ".join(people_tags + other_tags) return output_prompt def sort_taglist(tags: list[str]): if not tags: return [] character_tags: list[str] = [] series_tags: list[str] = [] people_tags: list[str] = [] group_list = ['groups', 'body_parts', 'attire', 'posture', 'objects', 'creatures', 'locations', 'disambiguation_pages', 'commonly_misused_tags', 'phrases', 'verbs_and_gerunds', 'subjective', 'nudity', 'sex_objects', 'sex', 'sex_acts', 'image_composition', 'artistic_license', 'text', 'year_tags', 'metatags'] group_tags = {} other_tags: list[str] = [] rating_tags: list[str] = [] group_dict = tag_group_dict group_set = set(group_dict.keys()) character_set = set(anime_series_dict.keys()) series_set = set(anime_series_dict.values()) rating_set = set(DANBOORU_TO_E621_RATING_MAP.keys()) | set(DANBOORU_TO_E621_RATING_MAP.values()) for tag in tags: tag = tag.strip().replace("_", " ") if tag in PEOPLE_TAGS: people_tags.append(tag) elif tag in rating_set: rating_tags.append(tag) elif tag in group_set: elem = group_dict[tag] group_tags[elem] = group_tags[elem] + [tag] if elem in group_tags else [tag] elif tag in character_set: character_tags.append(tag) elif tag in series_set: series_tags.append(tag) else: other_tags.append(tag) output_group_tags: list[str] = [] for k in group_list: output_group_tags.extend(group_tags.get(k, [])) rating_tags = [rating_tags[0]] if rating_tags else [] rating_tags = ["explicit, nsfw"] if rating_tags and rating_tags[0] == "explicit" else rating_tags output_tags = character_tags + series_tags + people_tags + output_group_tags + other_tags + rating_tags return output_tags def sort_tags(tags: str): if not tags: return "" taglist: list[str] = [] for tag in tags.split(","): taglist.append(tag.strip()) taglist = list(filter(lambda x: x != "", taglist)) return ", ".join(sort_taglist(taglist)) def postprocess_results(results: dict[str, float], general_threshold: float, character_threshold: float): results = { k: v for k, v in sorted(results.items(), key=lambda item: item[1], reverse=True) } rating = {} character = {} general = {} for k, v in results.items(): if k.startswith("rating:"): rating[k.replace("rating:", "")] = v continue elif k.startswith("character:"): character[k.replace("character:", "")] = v continue general[k] = v character = {k: v for k, v in character.items() if v >= character_threshold} general = {k: v for k, v in general.items() if v >= general_threshold} return rating, character, general def gen_prompt(rating: list[str], character: list[str], general: list[str]): people_tags: list[str] = [] other_tags: list[str] = [] rating_tag = RATING_MAP[rating[0]] for tag in general: if tag in PEOPLE_TAGS: people_tags.append(tag) else: other_tags.append(tag) all_tags = people_tags + other_tags return ", ".join(all_tags) @spaces.GPU() def predict_tags(image: Image.Image, general_threshold: float = 0.3, character_threshold: float = 0.8): inputs = wd_processor.preprocess(image, return_tensors="pt") outputs = wd_model(**inputs.to(wd_model.device, wd_model.dtype)) logits = torch.sigmoid(outputs.logits[0]) # take the first logits # get probabilities results = { wd_model.config.id2label[i]: float(logit.float()) for i, logit in enumerate(logits) } # rating, character, general rating, character, general = postprocess_results( results, general_threshold, character_threshold ) prompt = gen_prompt( list(rating.keys()), list(character.keys()), list(general.keys()) ) output_series_tag = "" output_series_list = character_list_to_series_list(character.keys()) if output_series_list: output_series_tag = output_series_list[0] else: output_series_tag = "" return output_series_tag, ", ".join(character.keys()), prompt, gr.update(interactive=True), def predict_tags_wd(image: Image.Image, input_tags: str, algo: list[str], general_threshold: float = 0.3, character_threshold: float = 0.8): if not "Use WD Tagger" in algo and len(algo) != 0: return "", "", input_tags, gr.update(interactive=True), return predict_tags(image, general_threshold, character_threshold) def compose_prompt_to_copy(character: str, series: str, general: str): characters = character.split(",") if character else [] serieses = series.split(",") if series else [] generals = general.split(",") if general else [] tags = characters + serieses + generals cprompt = ",".join(tags) if tags else "" return cprompt