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from typing import Dict, Any, List
import re
class PromptEnhancer:
def __init__(self):
self.quality_terms = {
"Ultra Réaliste": [
"masterpiece", "best quality", "ultra realistic",
"photorealistic", "8k uhd", "high resolution",
"detailed", "sharp focus", "professional photography"
],
"Artistique Pro": [
"masterpiece", "best quality", "professional",
"detailed", "artistic", "perfect composition",
"award winning", "trending on artstation"
]
}
def enhance(self, prompt: str, style: str, composition: str, mood: str) -> str:
"""Améliore le prompt en ajoutant des termes de qualité et de style appropriés"""
# Nettoyage et normalisation du prompt
cleaned_prompt = self._clean_prompt(prompt)
# Ajout des termes de qualité spécifiques au style
quality_terms = self.quality_terms.get(style, self.quality_terms["Ultra Réaliste"])
quality_string = ", ".join(quality_terms)
# Construction du prompt final
enhanced_prompt = f"{cleaned_prompt}, {quality_string}, {composition}, {mood}"
# Optimisation finale
return self._optimize_prompt(enhanced_prompt)
def _clean_prompt(self, prompt: str) -> str:
"""Nettoie et normalise le prompt"""
# Suppression des espaces multiples
cleaned = re.sub(r'\s+', ' ', prompt.strip())
# Suppression des termes de qualité redondants
redundant_terms = ["high quality", "good quality", "best quality", "hq"]
for term in redundant_terms:
cleaned = re.sub(rf'\b{term}\b', '', cleaned, flags=re.IGNORECASE)
return cleaned.strip()
def _optimize_prompt(self, prompt: str) -> str:
"""Optimisation finale du prompt"""
# Limitation de la longueur
words = prompt.split()
if len(words) > 77: # SDXL peut gérer jusqu'à 77 tokens
words = words[:77]
# Réorganisation pour mettre les termes importants en premier
important_terms = []
regular_terms = []
for word in words:
if word.lower() in ["masterpiece", "best quality", "professional"]:
important_terms.append(word)
else:
regular_terms.append(word)
return ", ".join(important_terms + regular_terms) |