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from PIL import Image
import torch
from transformers import (
AutoImageProcessor,
AutoModelForImageClassification,
)
import gradio as gr
import spaces # ZERO GPU
MODEL_NAMES = ["p1atdev/wd-swinv2-tagger-v3-hf"]
MODEL_NAME = MODEL_NAMES[0]
model = AutoModelForImageClassification.from_pretrained(MODEL_NAME, trust_remote_code=True)
model.to("cuda" if torch.cuda.is_available() else "cpu")
processor = AutoImageProcessor.from_pretrained(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 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
def get_series_dict():
import re
with open('characterfull.txt', 'r') as f:
lines = f.readlines()
series_dict = {}
for line in lines:
parts = line.strip().split(', ')
if len(parts) >= 3:
name = parts[-2].replace("\\", "")
if name.endswith(")"):
names = name.split("(")
character_name = "(".join(names[:-1])
if character_name.endswith(" "):
name = character_name[:-1]
series = re.sub(r'\\[()]', '', parts[-1])
series_dict[name] = series
return series_dict
anime_series_dict = get_series_dict()
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 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[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
tags_to_ja_dict = load_dict_from_csv('all_tags_ja_ext.csv')
def convert_tags_to_ja(input_prompt: str = ""):
tags = input_prompt.split(",") if input_prompt else []
out_tags = []
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)
def insert_recom_prompt(prompt: str = "", neg_prompt: str = "", type: str = "None"):
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)
animagine_ps = to_list("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")
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 == "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
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 = ['people', 'age', 'pattern', 'place', 'hair', 'modifier', 'screen', 'animal', 'effect', 'situation', 'status', 'lighting', 'accesory', 'body', 'nsfw', 'camera', 'option', 'taste', 'other', 'detail', 'action', 'dress', 'character', 'face', 'costume', 'attribute', 'weather', 'temporary', 'gender', 'favorite', 'food', 'object', 'quality', 'expression', 'life', 'background']
keep_group_dict = {
"body": ['people', 'age', 'hair', 'body', 'character', 'face', 'gender'],
"dress": ['people', 'age', 'hair', 'accesory', 'body', 'dress', 'character', 'face', 'costume', 'gender'],
"all": ['people', 'age', 'pattern', 'place', 'hair', 'modifier', 'screen', 'animal', 'effect', 'situation', 'status', 'lighting', 'accesory', 'body', 'nsfw', 'camera', 'option', 'taste', 'other', 'detail', 'action', 'dress', 'character', 'face', 'costume', 'attribute', 'weather', 'temporary', 'gender', 'favorite', 'food', 'object', 'quality', 'expression', 'life', 'background']
}
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, ['people', 'age', 'hair', 'body', 'character', 'face', 'gender'])
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 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 = processor.preprocess(image, return_tensors="pt")
outputs = model(**inputs.to(model.device, model.dtype))
logits = torch.sigmoid(outputs.logits[0]) # take the first logits
# get probabilities
results = {
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 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
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