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@@ -107,3 +107,4 @@ Danbooru[[:space:]]Prompt[[:space:]]Selector/csv2023_nsfw.csv filter=lfs diff=lf
 Danbooru[[:space:]]Prompt[[:space:]]Selector/csv2023_safeimage.csv filter=lfs diff=lfs merge=lfs -text
 Danbooru[[:space:]]Prompt[[:space:]]Selector/csv2023_sensitive.csv filter=lfs diff=lfs merge=lfs -text
 Danbooru[[:space:]]Prompt[[:space:]]Selector/TEST2024/NAIA_1231_console_testv1.exe filter=lfs diff=lfs merge=lfs -text
+Danbooru[[:space:]]Prompt[[:space:]]Selector/TEST2024/NAIA_0101_console_testv2.exe filter=lfs diff=lfs merge=lfs -text

Danbooru Prompt Selector/TEST2024/NAIA_0101_console_testv2.exe

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e7e2af30547a1146aa983e950ca89d891f3ef108da60b6d2b5906cc802d9ab25
+size 837862738

Danbooru Prompt Selector/TEST2024/NAIA_generation.py

@@ -186,7 +186,7 @@ def generate(gen_request):
         "seed": gen_request["seed"],
         "extra_noise_seed": random.randint(0,9999999999),
         "sampler": gen_request["sampler"],
-        "steps": 28,
+        "steps": 28 if gen_request["type"]!="upper" else gen_request["steps"],
         "scale": gen_request["scale"],
         "uncond_scale": 1.0,
         "negative_prompt": gen_request["negative"],

Danbooru Prompt Selector/TEST2024/NAIA_random_function_core.py

@@ -42,7 +42,7 @@ def RFP(popped_row, fix_prompt, after_prompt, auto_hide_prompt, rm_a, rm_s, rm_c
         series = [item.strip() for item in popped_row['copyright'].split(',')]
         fix = fix + series
     after = [item.strip() for item in after_prompt[:-1].split(',')]
-    auto_hide = [item.strip() for item in auto_hide_prompt[:-1].split(',')]
+    auto_hide = [item.strip() for item in auto_hide_prompt[:-1].split(',')] + ["| |", ":|", "\||/", "<|> <|>", "|| ||", ";|"]
     fix_index = find_keyword_index(general)
     processed = general.copy()
     temp_hide_prompt = []
@@ -67,4 +67,21 @@ def RFP(popped_row, fix_prompt, after_prompt, auto_hide_prompt, rm_a, rm_s, rm_c
     if magic_word["random_artist"] == True:
         processed.insert(fix_index, magic_word["random_artist_name"])
         
+    boy_in_processed = girl_in_processed = None
+    for boy in boys:
+        if boy in processed:
+            boy_in_processed = boy
+            break
+
+    for girl in girls:
+        if girl in processed:
+            girl_in_processed = girl
+            break
+        
+    if boy_in_processed and girl_in_processed:
+        boy_index = processed.index(boy_in_processed)
+        girl_index = processed.index(girl_in_processed)
+        if boy_index > girl_index:
+            processed.pop(boy_index)
+            processed.insert(girl_index, boy_in_processed)
     return ', '.join(processed)

Danbooru Prompt Selector/TEST2024/NAIA_search.py

@@ -1,34 +1,45 @@
-import customtkinter
 import pandas as pd
+import re
 
 # 모든 키워드를 포함하는 행만 필터링하는 함수
 def filter_rows_containing_all_keywords(df, keywords):
+    special_chars = r".^$*+?{}[]\|()"
     # 모든 키워드에 대한 boolean mask 초기화, df의 인덱스를 사용
     final_mask = pd.Series([True] * len(df), index=df.index)
 
     # 각 키워드에 대해 DataFrame의 모든 열을 검사하고 boolean mask 생성 및 저장
     for keyword in keywords:
         keyword_mask = pd.Series([False] * len(df), index=df.index)
+        request_regex = False
+        if any(char in keyword for char in special_chars): 
+            keyword = re.escape(keyword)
+            request_regex = True
 
         for column in ['copyright', 'character', 'artist', 'meta', 'general']:
             if df[column].dtype == 'object':
-                keyword_mask |= df[column].str.contains(keyword, na=False)
+                if request_regex: keyword_mask |= df[column].str.contains(keyword, na=False, regex=True)
+                else: keyword_mask |= df[column].str.contains(keyword, na=False)
 
         final_mask &= keyword_mask
 
     return df[final_mask]
 
 def filter_rows_not_containing_all_keywords(df, keywords):
+    special_chars = r".^$*+?{}[]\|()"
     # 모든 키워드를 포함하지 않는 행을 필터링하기 위한 boolean mask 초기화, df의 인덱스를 사용
     final_mask = pd.Series([True] * len(df), index=df.index)
 
     # 각 키워드에 대해 DataFrame의 모든 열을 검사하고 boolean mask 생성 및 저장
     for keyword in keywords:
         keyword_mask = pd.Series([False] * len(df), index=df.index)
-
+        request_regex = False
+        if any(char in keyword for char in special_chars): 
+            keyword = re.escape(keyword)
+            request_regex = True
         for column in ['copyright', 'character', 'artist', 'meta', 'general']:
             if df[column].dtype == 'object':
-                keyword_mask |= df[column].str.contains(keyword, na=False)
+                if request_regex: keyword_mask |= df[column].str.contains(keyword, na=False, regex=True)
+                else: keyword_mask |= df[column].str.contains(keyword, na=False)
 
         # 모든 키워드를 포함하는 행에 대한 mask를 반전시켜 final_mask에 저장
         final_mask &= ~keyword_mask
@@ -36,18 +47,25 @@ def filter_rows_not_containing_all_keywords(df, keywords):
     return df[final_mask]
 
 def process_asterisk_group(df, asterisk_group):
+    special_chars = r".^$*+?{}[]\|()"
     # 각 키워드 앞의 '*'를 제거하고 맨 뒤에 ',' 추가
     asterisk_keywords = [keyword.lstrip('*') + ',' for keyword in asterisk_group]
 
     # 각 행에 대해 임시 문자열 search_string을 만들고 검색 수행
     df['search_string'] = df[['copyright', 'character', 'artist', 'meta', 'general']].apply(lambda x: ' ' + ', '.join(x.astype(str)) + ',', axis=1)
     for keyword in asterisk_keywords:
-        df = df[df['search_string'].str.contains(keyword, na=False)]
+        request_regex = False
+        if any(char in keyword for char in special_chars): 
+            keyword = re.escape(keyword)
+            request_regex = True
+        if request_regex: df = df[df['search_string'].str.contains(keyword, na=False, regex=True)]
+        else:  df = df[df['search_string'].str.contains(keyword, na=False)]
     df.drop('search_string', axis=1, inplace=True)
 
     return df
 
 def process_perfect_negative_group(df, perfect_negative_group):
+    special_chars = r".^$*+?{}[]\|()"
     # 각 키워드 앞의 '~'를 제거하고 맨 뒤에 ',' 추가
     perfect_negative_keywords = [keyword.lstrip('~') + ',' for keyword in perfect_negative_group]
 
@@ -57,7 +75,12 @@ def process_perfect_negative_group(df, perfect_negative_group):
     # 모든 키워드에 대한 검색 결과를 하나의 boolean Series로 결합
     combined_mask = pd.Series([True] * len(df), index=df.index)
     for keyword in perfect_negative_keywords:
-        keyword_mask = df['search_string'].str.contains(keyword, na=False)
+        request_regex = False
+        if any(char in keyword for char in special_chars): 
+            keyword = re.escape(keyword)
+            request_regex = True
+        if request_regex: keyword_mask = df['search_string'].str.contains(keyword, na=False, regex=True)
+        else: df['search_string'].str.contains(keyword, na=False)
         combined_mask &= ~keyword_mask
 
     # 최종적으로 일치하지 않는 행만 필터링
@@ -80,6 +103,8 @@ def search(df, search_request, exclude_request, E, N, S, G):
     if(len(df) == 0):
         return None
     
+    special_chars = r".^$*+?{}[]\|()"
+
     #search_request에 대한 처리
     #처리순서 normal -> curly -> asterisk
     split_requests = [item.strip() for item in search_request.split(',')]
@@ -87,10 +112,11 @@ def search(df, search_request, exclude_request, E, N, S, G):
     curly_brace_group = [item for item in split_requests if item.startswith('{') and item.endswith('}')]
     asterisk_group = [item for item in split_requests if item.startswith('*')]
     normal_group = [item for item in split_requests if item not in curly_brace_group + asterisk_group]
-
+    #normal_group = [re.escape(item) if any(char in item for char in special_chars) else item for item in normal_group]
     negative_split_requests = [item.strip() for item in exclude_request.split(',')]
     perfect_negative_group = [item for item in negative_split_requests if item.startswith('~')]
     negative_group = [item for item in negative_split_requests if item not in perfect_negative_group]
+    #negative_group = [re.escape(item) if any(char in item for char in special_chars) else item for item in negative_group]
 
     if '' in split_requests:
         split_requests.remove('')
@@ -113,8 +139,13 @@ def search(df, search_request, exclude_request, E, N, S, G):
                 for keyword in or_search_keyword:
                     if keyword.startswith('*'):
                         keyword = keyword[1:]
+                    request_regex = False
+                    if any(char in keyword for char in special_chars): 
+                        keyword = re.escape(keyword)
+                        request_regex = True
                     for column in ['copyright', 'character', 'artist', 'meta', 'general']:
-                        matched_rows = df[df[column].str.contains(keyword, na=False)]
+                        if request_regex: matched_rows = df[df[column].str.contains(keyword, na=False, regex=True)]
+                        else: matched_rows = df[df[column].str.contains(keyword, na=False)]
                         if not matched_rows.empty:
                             results = pd.concat([results, matched_rows])
                             break