feat: allow multiple matches for each token, optimize and extract method cleanup_prompt

modules/util.py

@@ -12,15 +12,15 @@ import hashlib
 
 from PIL import Image
 
+import modules.config
 import modules.sdxl_styles
 
 LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS)
 
-
 # Regexp compiled once. Matches entries with the following pattern:
 # <lora:some_lora:1>
 # <lora:aNotherLora:-1.6>
-LORAS_PROMPT_PATTERN = re.compile(r".* <lora : ([^:]+) : ([+-]? (?: (?:\d+ (?:\.\d*)?) | (?:\.\d+)))> .*", re.X)
+LORAS_PROMPT_PATTERN = re.compile(r"(<lora:([^:]+):([+-]?(?:\d+(?:\.\d*)?|\.\d+))>)", re.X)
 
 HASH_SHA256_LENGTH = 10
 
@@ -372,26 +372,57 @@ def get_file_from_folder_list(name, folders):
     return os.path.abspath(os.path.realpath(os.path.join(folders[0], name)))
 
 
+def get_enabled_loras(loras: list, remove_none=True) -> list:
+    return [(lora[1], lora[2]) for lora in loras if lora[0] and (lora[1] != 'None' if remove_none else True)]
 
 
-def get_enabled_loras(loras: list) -> list:
-    return [(lora[1], lora[2]) for lora in loras if lora[0]]
+def parse_lora_references_from_prompt(prompt: str, loras: List[Tuple[AnyStr, float]], loras_limit: int = 5,
+                                      prompt_cleanup=True, deduplicate_loras=True) -> tuple[List[Tuple[AnyStr, float]], str]:
+    found_loras = []
+    prompt_without_loras = ""
+    for token in prompt.split(" "):
+        matches = LORAS_PROMPT_PATTERN.findall(token)
 
+        if matches:
+            for match in matches:
+                found_loras.append((f"{match[1]}.safetensors", float(match[2])))
+                prompt_without_loras += token.replace(match[0], '')
+        else:
+            prompt_without_loras += token
+        prompt_without_loras += ' '
+
+    cleaned_prompt = prompt_without_loras[:-1]
+    if prompt_cleanup:
+        cleaned_prompt = cleanup_prompt(prompt_without_loras)
 
-def parse_lora_references_from_prompt(prompt: str, loras: List[Tuple[AnyStr, float]], loras_limit: int = 5) -> List[Tuple[AnyStr, float]]:
     new_loras = []
+    lora_names = [lora[0] for lora in loras]
+    for found_lora in found_loras:
+        if deduplicate_loras and found_lora[0] in lora_names:
+            continue
+        new_loras.append(found_lora)
+
+    if len(new_loras) == 0:
+        return loras, cleaned_prompt
+
     updated_loras = []
-    for token in prompt.split(","):
-        m = LORAS_PROMPT_PATTERN.match(token)
-
-        if m:
-            new_loras.append((f"{m.group(1)}.safetensors", float(m.group(2))))
-
     for lora in loras + new_loras:
         if lora[0] != "None":
             updated_loras.append(lora)
 
-    return updated_loras[:loras_limit]
+    return updated_loras[:loras_limit], cleaned_prompt
+
+
+def cleanup_prompt(prompt):
+    prompt = re.sub(' +', ' ', prompt)
+    prompt = re.sub(',+', ',', prompt)
+    cleaned_prompt = ''
+    for token in prompt.split(','):
+        token = token.strip()
+        if token == '':
+            continue
+        cleaned_prompt += token + ', '
+    return cleaned_prompt[:-2]
 
 
 def apply_wildcards(wildcard_text, rng, i, read_wildcards_in_order) -> str: