Update app.py

app.py

@@ -1,498 +1,86 @@
-import os
 import spaces
 import gradio as gr
 import numpy as np
+import PIL.Image
 from PIL import Image
 import random
-from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler
+from diffusers import StableDiffusionXLPipeline
+from diffusers import EulerAncestralDiscreteScheduler
 import torch
-from transformers import pipeline as transformers_pipeline
-import re
-from cohere import ClientV2  # Changed from HuggingFace to Cohere
+from compel import Compel, ReturnedEmbeddingsType
 
-# ------------------------------------------------------------
-# DEVICE SETUP
-# ------------------------------------------------------------
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
-# ------------------------------------------------------------
-# STABLE DIFFUSION XL PIPELINE
-# ------------------------------------------------------------
+# Make sure to use torch.float16 consistently throughout the pipeline
 pipe = StableDiffusionXLPipeline.from_pretrained(
     "votepurchase/waiREALCN_v14",
     torch_dtype=torch.float16,
-    variant="fp16",
-    use_safetensors=True,
+    variant="fp16",  # Explicitly use fp16 variant
+    use_safetensors=True  # Use safetensors if available
 )
+
 pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
 pipe.to(device)
 
-# Force important sub-modules to fp16 for VRAM efficiency
-for sub in (pipe.text_encoder, pipe.text_encoder_2, pipe.vae, pipe.unet):
-    sub.to(torch.float16)
-
-# ------------------------------------------------------------
-# INITIALIZE COHERE CLIENT FOR TRANSLATIONS AND PROMPT GENERATION
-# ------------------------------------------------------------
-coh_api_key = os.getenv("COH_API")
-if not coh_api_key:
-    print("[WARNING] COH_API environment variable not found. LLM features will not work.")
-    coh_client = None
-else:
-    try:
-        coh_client = ClientV2(api_key=coh_api_key)
-        print("[INFO] Cohere client initialized successfully.")
-    except Exception as e:
-        print(f"[ERROR] Failed to initialize Cohere client: {str(e)}")
-        coh_client = None
-
-
-# 1. 비영어 문자 감지 정규식을 더 명확하게 수정
-# 한글, 일본어, 중국어를 명시적으로 포함
-non_english_regex = re.compile(r'[\uac00-\ud7a3\u3040-\u30ff\u3400-\u4dbf\u4e00-\u9fff\uf900-\ufaff]+')
-
-def is_non_english(text):
-    """명확하게 비영어 텍스트 여부를 확인하는 함수"""
-    # 한글 확인 (AC00-D7A3)
-    if re.search(r'[\uac00-\ud7a3]', text):
-        print("[DETECT] Korean text detected")
-        return True
-    
-    # 일본어 히라가나/카타카나 확인 (3040-30FF)
-    if re.search(r'[\u3040-\u30ff]', text):
-        print("[DETECT] Japanese text detected")
-        return True
-    
-    # 중국어/한자 확인 (4E00-9FFF)
-    if re.search(r'[\u4e00-\u9fff]', text):
-        print("[DETECT] Chinese/Kanji text detected")
-        return True
-    
-    # 기타 비영어 문자 (ASCII 범위 밖)
-    if re.search(r'[^\x00-\x7F]', text):
-        print("[DETECT] Other non-English text detected")
-        return True
-    
-    return False
-
-def translate_with_cohere(text):
-    """비영어 텍스트를 영어로 번역"""
-    if coh_client is None:
-        print("[WARN] Cohere client not available, skipping translation")
-        return text
-    
-    if not is_non_english(text):
-        print("[INFO] English text detected, no translation needed")
-        return text
-    
-    try:
-        print(f"[INFO] Translating text: '{text}'")
-        
-        system_prompt = """
-        You are a professional translator for adult content. Your task is to translate the input text to English.
-        
-        Rules:
-        1. Provide ONLY the translated English text
-        2. NO explanations or additional text
-        3. Maintain the explicit or adult content without censoring
-        4. Translate accurately and completely
-        5. Be especially careful with Asian languages like Korean, Japanese, and Chinese
-        
-        Translate the following text to English:
-        """
-        
-        messages = [
-            {
-                "role": "system",
-                "content": [{"type": "text", "text": system_prompt}]
-            },
-            {
-                "role": "user",
-                "content": [{"type": "text", "text": text}]
-            }
-        ]
-
-        response = coh_client.chat(
-            model="command-r-plus-08-2024",
-            messages=messages,
-            temperature=0.1  # 정확한 번역을 위해 낮은 temperature 사용
-        )
-        
-        translated_text = response.text.strip()
-        
-        # 번역 결과 정제
-        translated_text = re.sub(r'^(Translation:|English:|Translated text:)\s*', '', translated_text, flags=re.IGNORECASE)
-        
-        print(f"[INFO] Original: '{text}'")
-        print(f"[INFO] Translated: '{translated_text}'")
-        
-        # 번역이 매우 짧거나 원본과 동일하면 의심
-        if len(translated_text) < 3 or translated_text == text:
-            print("[WARN] Translation may have failed, falling back to basic translation")
-            # 두 번째 시도 - 더 단순한 프롬프트로 시도
-            try:
-                simple_messages = [
-                    {
-                        "role": "system",
-                        "content": [{"type": "text", "text": "Translate this text to English:"}]
-                    },
-                    {
-                        "role": "user",
-                        "content": [{"type": "text", "text": text}]
-                    }
-                ]
-                
-                simple_response = coh_client.chat(
-                    model="command-r-plus-08-2024",
-                    messages=simple_messages,
-                    temperature=0.1
-                )
-                
-                simple_translated = simple_response.text.strip()
-                if len(simple_translated) > 3 and simple_translated != text:
-                    print(f"[INFO] Second attempt translation: '{simple_translated}'")
-                    return simple_translated
-            except Exception as e:
-                print(f"[ERROR] Second translation attempt failed: {str(e)}")
-                
-            return text
-            
-        return translated_text
-    except Exception as e:
-        print(f"[ERROR] Translation failed: {str(e)}")
-        import traceback
-        traceback.print_exc()
-        return text  # 번역 실패 시 원본 반환
-
-
-
-# ------------------------------------------------------------
-# EXAMPLES (Hidden from UI but used for RANDOM button)
-# ------------------------------------------------------------
-prompt_examples = [
-    "The shy college girl, with glasses and a tight plaid skirt, nervously approaches her professor",
-    "Her skirt rose a little higher with each gentle push, a soft blush of blush spreading across her cheeks as she felt the satisfying warmth of his breath on her cheek.",
-    "a girl in a school uniform having her skirt pulled up by a boy, and then being fucked",
-    "Moody mature anime scene of two lovers fuck under neon rain, sensual atmosphere",
-    "Moody mature anime scene of two lovers kissing under neon rain, sensual atmosphere",
-    "The girl sits on the boy's lap by the window, his hands resting on her waist. She is unbuttoning his shirt, her expression focused and intense.",
-    "A girl with long, black hair is sleeping on her desk in the classroom. Her skirt has ridden up, revealing her thighs, and a trail of drool escapes her slightly parted lips.",
-    "The waves rolled gently, a slow, sweet kiss of the lip, a slow, slow build of anticipation as their toes bumped gently – a slow, sweet kiss of the lip, a promise of more to come.",
-    "Her elegant silk gown swayed gracefully as she approached him, the delicate fabric brushing against her legs. A warm blush spread across her cheeks as she felt his breath on her face.",
-    "Her white blouse and light cotton skirt rose a little higher with each gentle push, a soft blush spreading across her cheeks as she felt the satisfying warmth of his breath on her cheek.",
-    "A woman in a business suit having her skirt lifted by a man, and then being sexually assaulted.",
-    "The older woman sits on the man's lap by the fireplace, his hands resting on her hips. She is unbuttoning his vest, her expression focused and intense. He takes control of the situation as she finishes unbuttoning his shirt, pushing her onto her back and begins to have sex with her.",
-    "There is a woman with long black hair. Her face features alluring eyes and full lips, with a slender figure adorned in black lace lingerie. She lies on the bed, loosening her lingerie strap with one hand while seductively glancing downward.",
-    "In a dimly lit room, the same woman teases with her dark, flowing hair, now covering her voluptuous breasts, while a black garter belt accentuates her thighs. She sits on the sofa, leaning back, lifting one leg to expose her most private areas through the sheer lingerie.",
-    "A woman with glasses, lying on the bed in just her bra, spreads her legs wide, revealing all! She wears a sultry expression, gazing directly at the viewer with her brown eyes, her short black hair cascading over the pillow. Her slim figure, accentuated by the lacy lingerie, exudes a seductive aura.",
-    "A soft focus on the girl's face, eyes closed, biting her lip, as her roommate performs oral pleasure, the experienced woman's hair cascading between her thighs.",
-    "A woman in a blue hanbok sits on a wooden floor, her legs folded beneath her, gazing out of a window, the sunlight highlighting the graceful lines of her clothing.",
-    "The couple, immersed in a wooden outdoor bath, share an intimate moment, her wet kimono clinging to her curves, his hands exploring her body beneath the water's surface.",
-    "A steamy shower scene, the twins embrace under the warm water, their soapy hands gliding over each other's curves, their passion intensifying as they explore uncharted territories.",
-    "The teacher, with a firm grip, pins the student against the blackboard, her skirt hiked up, exposing her delicate lace panties. Their heavy breathing echoes in the quiet room as they share an intense, intimate moment.",
-    "After hours, the girl sits on top of the teacher's lap, riding him on the classroom floor, her hair cascading over her face as she moves with increasing intensity, their bodies glistening with sweat.",
-    "In the dimly lit dorm room, the roommates lay entangled in a passionate embrace, their naked bodies glistening with sweat, as the experienced woman teaches her lover the art of kissing and touching.",
-    "The once-innocent student, now confident, takes charge, straddling her lover on the couch, their bare skin illuminated by the warm glow of the sunset through the window.",
-    "A close-up of the secretary's hand unzipping her boss's dress shirt, her fingers gently caressing his chest, their eyes locked in a heated embrace in the supply closet.",
-    "The secretary, in a tight pencil skirt and silk blouse, leans back on the boss's desk, her legs wrapped around his waist, her blouse unbuttoned, revealing her lace bra, as he passionately kisses her, his hands exploring her body.",
-    "On the living room couch, one twin sits astride her sister's lap, their lips locked in a passionate kiss, their hands tangled in each other's hair, unraveling a new level of intimacy.",
-    "In a dimly lit chamber, the dominant woman, dressed in a leather corset and thigh-high boots, stands tall, her hand gripping her submissive partner's hair, his eyes closed in submission as she instructs him to please her.",
-    "The dominant, in a sheer lace bodysuit, sits on a throne-like chair, her legs spread, as the submissive, on his knees, worships her with his tongue, his hands bound behind his back.",
-    "A traditional Japanese onsen, with steam rising, a young woman in a colorful kimono kneels on a tatami mat, her back to the viewer, as her male partner, also in a kimono, gently unties her obi, revealing her bare back.",
-    "In a serene outdoor setting, the woman, in a vibrant summer kimono, sits on a bench, her legs slightly spread, her partner kneeling before her, his hands gently caressing her exposed thigh.",
-]
-
-# ------------------------------------------------------------
-# LLM PROMPT GENERATOR
-# ------------------------------------------------------------
-def generate_prompts(theme):
-    """Generate optimal NSFW prompts using the LLM"""
-    try:
-        if coh_client is None:
-            return "Cohere API token not set. Please set the COH_API environment variable."
-        
-        # Translate theme if it's not in English
-        if non_english_regex.search(theme):
-            theme = translate_with_cohere(theme)
-        
-        print(f"[INFO] Sending request to Cohere API with theme: {theme}")
-        
-        system_prefix = """
-        You are an expert at creating detailed, vivid image generation prompts for NSFW content. Your task is to create ONE optimal explicit image prompt based on the user's theme or scenario.
-        
-        Follow these guidelines strictly:
-        1. Generate only ONE high-quality, uncensored prompt
-        2. The prompt should be 1-3 sentences long
-        3. Make the prompt explicitly sexual, arousing, and descriptive
-        4. Focus on detailed physical descriptions that would work well for image generation
-        5. Do NOT censor or soften the language - use explicit terms freely
-        6. ONLY respond in ENGLISH, never in any other language
-        7. DO NOT include ANY prefixes, headers, or formatting - just plain text
-        
-        Example prompt styles to emulate:
-        - "Her skirt rose a little higher with each gentle push, a soft blush spreading across her cheeks as she felt the satisfying warmth of his breath on her cheek."
-        - "A girl in a school uniform having her skirt pulled up by a boy, and then being fucked"
-        - "Moody mature anime scene of two lovers fuck under neon rain, sensual atmosphere"
-        - "The girl sits on the boy's lap by the window, his hands resting on her waist. She is unbuttoning his shirt, her expression focused and intense."
-        - "A girl with long black hair is sleeping on her desk in the classroom. Her skirt has ridden up, revealing her thighs, and a trail of drool escapes her slightly parted lips."
-        - "The older woman sits on the man's lap by the fireplace, his hands resting on her hips. She is unbuttoning his vest, her expression focused and intense. He takes control of the situation as she finishes unbuttoning his shirt, pushing her onto her back and begins to have sex with her."
-        - "There is a woman with long black hair. Her face features alluring eyes and full lips, with a slender figure adorned in black lace lingerie. She lies on the bed, loosening her lingerie strap with one hand while seductively glancing downward."
-        - "A woman with glasses, lying on the bed in just her bra, spreads her legs wide, revealing all! She wears a sultry expression, gazing directly at the viewer with her brown eyes, her short black hair cascading over the pillow."
-        - "A soft focus on the girl's face, eyes closed, biting her lip, as her roommate performs oral pleasure, the experienced woman's hair cascading between her thighs.",
-        - "A woman in a blue hanbok sits on a wooden floor, her legs folded beneath her, gazing out of a window, the sunlight highlighting the graceful lines of her clothing.",
-        - "The couple, immersed in a wooden outdoor bath, share an intimate moment, her wet kimono clinging to her curves, his hands exploring her body beneath the water's surface.",
-        - "A steamy shower scene, the twins embrace under the warm water, their soapy hands gliding over each other's curves, their passion intensifying as they explore uncharted territories.",
-        - "The teacher, with a firm grip, pins the student against the blackboard, her skirt hiked up, exposing her delicate lace panties. Their heavy breathing echoes in the quiet room as they share an intense, intimate moment.",
-        - "After hours, the girl sits on top of the teacher's lap, riding him on the classroom floor, her hair cascading over her face as she moves with increasing intensity, their bodies glistening with sweat.",
-        - "In the dimly lit dorm room, the roommates lay entangled in a passionate embrace, their naked bodies glistening with sweat, as the experienced woman teaches her lover the art of kissing and touching.",
-        - "The once-innocent student, now confident, takes charge, straddling her lover on the couch, their bare skin illuminated by the warm glow of the sunset through the window.",
-        - "A close-up of the secretary's hand unzipping her boss's dress shirt, her fingers gently caressing his chest, their eyes locked in a heated embrace in the supply closet.",
-        - "The secretary, in a tight pencil skirt and silk blouse, leans back on the boss's desk, her legs wrapped around his waist, her blouse unbuttoned, revealing her lace bra, as he passionately kisses her, his hands exploring her body.",
-        - "On the living room couch, one twin sits astride her sister's lap, their lips locked in a passionate kiss, their hands tangled in each other's hair, unraveling a new level of intimacy.",
-        - "In a dimly lit chamber, the dominant woman, dressed in a leather corset and thigh-high boots, stands tall, her hand gripping her submissive partner's hair, his eyes closed in submission as she instructs him to please her.",
-        - "The dominant, in a sheer lace bodysuit, sits on a throne-like chair, her legs spread, as the submissive, on his knees, worships her with his tongue, his hands bound behind his back.",
-        - "A traditional Japanese onsen, with steam rising, a young woman in a colorful kimono kneels on a tatami mat, her back to the viewer, as her male partner, also in a kimono, gently unties her obi, revealing her bare back.",
-        - "In a serene outdoor setting, the woman, in a vibrant summer kimono, sits on a bench, her legs slightly spread, her partner kneeling before her, his hands gently caressing her exposed thigh.",
-           
-        Respond ONLY with the single prompt text in ENGLISH with NO PREFIXES of any kind.
-        """
-
-        # Format messages for Cohere API
-        messages = [
-            {
-                "role": "system", 
-                "content": [{"type": "text", "text": system_prefix}]
-            },
-            {
-                "role": "user",
-                "content": [{"type": "text", "text": theme}]
-            }
-        ]
-
-        # Generate response using Cohere
-        response = coh_client.chat(
-            model="command-r-plus-08-2024",
-            messages=messages,
-            temperature=0.8
-        )
-        
-        # Extract only the text content without any debug information
-        if hasattr(response, 'text'):
-            generated_prompt = response.text
-        else:
-            # Handle different response formats
-            try:
-                # Try to extract just the text content from the response
-                response_str = str(response)
-                # If it's a complex object with nested structure
-                if 'text=' in response_str:
-                    text_match = re.search(r"text=['\"]([^'\"]+)['\"]", response_str)
-                    if text_match:
-                        generated_prompt = text_match.group(1)
-                    else:
-                        generated_prompt = response_str
-                else:
-                    generated_prompt = response_str
-            except:
-                generated_prompt = str(response)
-        
-        # FORCE translation to English if there's any non-English content
-        if non_english_regex.search(generated_prompt):
-            print("[INFO] Translating non-English prompt to English")
-            generated_prompt = translate_with_cohere(generated_prompt)
-        
-        # Clean the prompt
-        generated_prompt = re.sub(r'^AI🐼:\s*', '', generated_prompt)
-        generated_prompt = re.sub(r'^\d+[\.\)]\s*', '', generated_prompt)
-        generated_prompt = re.sub(r'^(Prompt|Response|Result|Output):\s*', '', generated_prompt)
-        generated_prompt = re.sub(r'^["\']+|["\']+$', '', generated_prompt)
-        generated_prompt = generated_prompt.strip()
-        generated_prompt = re.sub(r'\s+', ' ', generated_prompt)
-        
-        print(f"[INFO] Generated prompt: {generated_prompt}")
-        
-        # Final verification - check length and ensure it's English
-        if len(generated_prompt) > 10:
-            return generated_prompt
-        else:
-            return "Failed to generate a valid prompt"
-        
-    except Exception as e:
-        print(f"[ERROR] Prompt generation failed: {str(e)}")
-        import traceback
-        traceback.print_exc()
-        return f"Error generating prompt: {str(e)}"
-
-
-
+# Force all components to use the same dtype
+pipe.text_encoder.to(torch.float16)
+pipe.text_encoder_2.to(torch.float16)
+pipe.vae.to(torch.float16)
+pipe.unet.to(torch.float16)
+
+# 追加: Initialize Compel for long prompt processing
+compel = Compel(
+    tokenizer=[pipe.tokenizer, pipe.tokenizer_2],
+    text_encoder=[pipe.text_encoder, pipe.text_encoder_2],
+    returned_embeddings_type=ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NON_NORMALIZED,
+    requires_pooled=[False, True],
+    truncate_long_prompts=False
+)
 
-# ------------------------------------------------------------
-# SDXL INFERENCE WRAPPER
-# ------------------------------------------------------------
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 1216
 
+# 追加: Simple long prompt processing function
+def process_long_prompt(prompt, negative_prompt=""):
+    """Simple long prompt processing using Compel"""
+    try:
+        conditioning, pooled = compel([prompt, negative_prompt])
+        return conditioning, pooled
+    except Exception as e:
+        print(f"Long prompt processing failed: {e}, falling back to standard processing")
+        return None, None
+    
 @spaces.GPU
 def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
-    """
-    중요: 프롬프트 텍스트에 한글이나 다른 비영어 문자가 있으면 반드시 영어로 번역해야 합니다.
-    """
-    print(f"[DEBUG] Original prompt received: '{prompt}'")
-    print(f"[DEBUG] Original negative prompt received: '{negative_prompt}'")
-    
-    # 한글/비영어 감지 및 번역 (prompt)
-    has_korean = bool(re.search(r'[\uac00-\ud7a3]', prompt))
-    has_non_english = bool(re.search(r'[^\x00-\x7F]', prompt))
-    
-    if has_korean or has_non_english:
-        print(f"[ALERT] 비영어 프롬프트 감지됨: '{prompt}'")
-        
-        # Cohere를 사용하여 직접 번역
-        if coh_client:
-            try:
-                # 번역용 시스템 프롬프트
-                trans_system = "You are a translator. Translate the following text to English accurately. Only provide the translation, no comments or explanations."
-                
-                # 번역 요청
-                trans_response = coh_client.chat(
-                    model="command-r-plus-08-2024",
-                    messages=[
-                        {"role": "system", "content": [{"type": "text", "text": trans_system}]},
-                        {"role": "user", "content": [{"type": "text", "text": prompt}]}
-                    ],
-                    temperature=0.1
-                )
-                
-                # 응답 처리 - 다양한 속성 접근 방법 시도
-                translated_prompt = None
-                
-                # 방법 1: response.text
-                try:
-                    if hasattr(trans_response, 'text'):
-                        translated_prompt = trans_response.text
-                        print("[DEBUG] 방법 1 (text 속성) 성공")
-                except:
-                    pass
-                
-                # 방법 2: response.response
-                if translated_prompt is None:
-                    try:
-                        if hasattr(trans_response, 'response'):
-                            translated_prompt = trans_response.response
-                            print("[DEBUG] 방법 2 (response 속성) 성공")
-                    except:
-                        pass
-                
-                # 방법 3: response dictionary access
-                if translated_prompt is None:
-                    try:
-                        # 응답이 dictionary인 경우
-                        if isinstance(trans_response, dict) and 'text' in trans_response:
-                            translated_prompt = trans_response['text']
-                            print("[DEBUG] 방법 3 (dictionary access) 성공")
-                    except:
-                        pass
-                
-                # 방법 4: 문자열 변환 후 파싱
-                if translated_prompt is None:
-                    try:
-                        response_str = str(trans_response)
-                        print(f"[DEBUG] Response structure: {response_str[:200]}...")
-                        
-                        # text= 패턴 찾기
-                        match = re.search(r"text=['\"](.*?)['\"]", response_str)
-                        if match:
-                            translated_prompt = match.group(1)
-                            print("[DEBUG] 방법 4 (정규식 파싱) 성공")
-                        
-                        # content 패턴 찾기
-                        if not translated_prompt and 'content=' in response_str:
-                            match = re.search(r"content=['\"](.*?)['\"]", response_str)
-                            if match:
-                                translated_prompt = match.group(1)
-                                print("[DEBUG] 방법 4.1 (content 정규식) 성공")
-                    except Exception as parse_err:
-                        print(f"[DEBUG] 정규식 파싱 오류: {parse_err}")
-                
-                # 최종 결과 확인
-                if translated_prompt:
-                    translated_prompt = translated_prompt.strip()
-                    print(f"[SUCCESS] 번역됨: '{prompt}' -> '{translated_prompt}'")
-                    prompt = translated_prompt
-                else:
-                    # 마지막 수단: 전체 응답 구조 로깅
-                    print(f"[DEBUG] Full response type: {type(trans_response)}")
-                    print(f"[DEBUG] Full response dir: {dir(trans_response)}")
-                    print(f"[DEBUG] Could not extract translation, keeping original prompt")
-            except Exception as e:
-                print(f"[ERROR] 프롬프트 번역 실패: {str(e)}")
-                import traceback
-                traceback.print_exc()
-                # 번역 실패 시 원본 유지
+    # 変更: Remove the 60-word limit warning and add long prompt check
+    use_long_prompt = len(prompt.split()) > 60 or len(prompt) > 300
     
-    # 한글/비영어 감지 및 번역 (negative_prompt)
-    has_korean = bool(re.search(r'[\uac00-\ud7a3]', negative_prompt))
-    has_non_english = bool(re.search(r'[^\x00-\x7F]', negative_prompt))
-    
-    if has_korean or has_non_english:
-        print(f"[ALERT] 비영어 네거티브 프롬프트 감지됨: '{negative_prompt}'")
-        
-        # Cohere를 사용하여 직접 번역 (위와 동일한 방식으로)
-        if coh_client:
-            try:
-                trans_system = "You are a translator. Translate the following text to English accurately. Only provide the translation, no comments or explanations."
-                
-                trans_response = coh_client.chat(
-                    model="command-r-plus-08-2024",
-                    messages=[
-                        {"role": "system", "content": [{"type": "text", "text": trans_system}]},
-                        {"role": "user", "content": [{"type": "text", "text": negative_prompt}]}
-                    ],
-                    temperature=0.1
-                )
-                
-                # 다양한 방법으로 응답 처리 (프롬프트 처리와 동일)
-                translated_negative = None
-                
-                # 각종 접근 방법 (동일한 로직 적용)
-                try:
-                    if hasattr(trans_response, 'text'):
-                        translated_negative = trans_response.text
-                    elif hasattr(trans_response, 'response'):
-                        translated_negative = trans_response.response
-                    elif isinstance(trans_response, dict) and 'text' in trans_response:
-                        translated_negative = trans_response['text']
-                    else:
-                        response_str = str(trans_response)
-                        match = re.search(r"text=['\"](.*?)['\"]", response_str)
-                        if match:
-                            translated_negative = match.group(1)
-                        elif 'content=' in response_str:
-                            match = re.search(r"content=['\"](.*?)['\"]", response_str)
-                            if match:
-                                translated_negative = match.group(1)
-                except Exception as parse_err:
-                    print(f"[DEBUG] 네거티브 파싱 오류: {parse_err}")
-                
-                if translated_negative:
-                    translated_negative = translated_negative.strip()
-                    print(f"[SUCCESS] 네거티브 번역됨: '{negative_prompt}' -> '{translated_negative}'")
-                    negative_prompt = translated_negative
-            except Exception as e:
-                print(f"[ERROR] 네거티브 프롬프트 번역 실패: {str(e)}")
-    
-    print(f"[INFO] 최종 사용될 프롬프트: '{prompt}'")
-    print(f"[INFO] 최종 사용될 네거티브 프롬프트: '{negative_prompt}'")
-
-    if len(prompt.split()) > 60:
-        print("[WARN] Prompt >60 words — CLIP may truncate it.")
-
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
 
     generator = torch.Generator(device=device).manual_seed(seed)
-
+    
     try:
+        # 追加: Try long prompt processing first if prompt is long
+        if use_long_prompt:
+            print("Using long prompt processing...")
+            conditioning, pooled = process_long_prompt(prompt, negative_prompt)
+            
+            if conditioning is not None:
+                output_image = pipe(
+                    prompt_embeds=conditioning[0:1],
+                    pooled_prompt_embeds=pooled[0:1],
+                    negative_prompt_embeds=conditioning[1:2],
+                    negative_pooled_prompt_embeds=pooled[1:2],
+                    guidance_scale=guidance_scale,
+                    num_inference_steps=num_inference_steps,
+                    width=width,
+                    height=height,
+                    generator=generator
+                ).images[0]
+                return output_image
+        
+        # Fall back to standard processing
         output_image = pipe(
             prompt=prompt,
             negative_prompt=negative_prompt,
@@ -500,204 +88,98 @@ def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance
             num_inference_steps=num_inference_steps,
             width=width,
             height=height,
-            generator=generator,
+            generator=generator
         ).images[0]
-        return output_image, seed
+        
+        return output_image
     except RuntimeError as e:
-        print(f"[ERROR] Diffusion failed → {e}")
-        return Image.new("RGB", (width, height), color=(0, 0, 0)), seed
+        print(f"Error during generation: {e}")
+        # Return a blank image with error message
+        error_img = Image.new('RGB', (width, height), color=(0, 0, 0))
+        return error_img
 
-# Function to select a random example prompt
-def get_random_prompt():
-    return random.choice(prompt_examples)
 
-# ------------------------------------------------------------
-# UI LAYOUT + THEME (Enhanced Visual Design)
-# ------------------------------------------------------------
 css = """
-body {background: linear-gradient(135deg, #f2e6ff 0%, #e6f0ff 100%); color: #222; font-family: 'Noto Sans', sans-serif;}
-#col-container {margin: 0 auto; max-width: 768px; padding: 15px; background: rgba(255, 255, 255, 0.8); border-radius: 15px; box-shadow: 0 8px 32px rgba(31, 38, 135, 0.2);}
-.gr-button {background: #7fbdf6; color: #fff; border-radius: 8px; transition: all 0.3s ease; font-weight: bold;}
-.gr-button:hover {background: #5a9ae6; transform: translateY(-2px); box-shadow: 0 5px 15px rgba(0,0,0,0.1);}
-#prompt-box textarea {font-size: 1.1rem; height: 9rem !important; background: #fff; color: #222; border-radius: 10px; border: 1px solid #d1c1e0;}
-.boost-btn {background: #ff7eb6; margin-top: 5px;}
-.boost-btn:hover {background: #ff5aa5;}
-.random-btn {background: #9966ff; margin-top: 5px;}
-.random-btn:hover {background: #8040ff;}
-.container {animation: fadeIn 0.5s ease-in-out;}
-.title {color: #6600cc; text-shadow: 1px 1px 2px rgba(0,0,0,0.1);}
-.gr-form {border: none !important; background: transparent !important;}
-.gr-input {border-radius: 8px !important;}
-.gr-slider {height: 12px !important;}
-.gr-slider .handle {height: 20px !important; width: 20px !important;}
-.panel {border-radius: 12px; overflow: hidden; box-shadow: 0 4px 15px rgba(0,0,0,0.1);}
-.gr-image {border-radius: 12px; overflow: hidden; transition: all 0.3s ease;}
-.gr-image:hover {transform: scale(1.02); box-shadow: 0 8px 25px rgba(0,0,0,0.15);}
-@keyframes fadeIn {
-  from {opacity: 0; transform: translateY(20px);}
-  to {opacity: 1; transform: translateY(0);}
+#col-container {
+    margin: 0 auto;
+    max-width: 520px;
 }
-.gr-accordion {border-radius: 10px; overflow: hidden; transition: all 0.3s ease;}
-.gr-accordion:hover {box-shadow: 0 5px 15px rgba(0,0,0,0.1);}
 """
 
+with gr.Blocks(css=css) as demo:
 
-author_note = (
-    "**ℹ️ This code provides a test environment to experiment with the censorship limits and technical possibilities of AI-based adult content generation. It serves as an educational tool that offers researchers and creators the opportunity to test various prompt methods and understand the ethical boundaries of image generation technology. Uncensored, natural motion video generation service has also been developed but will be released in the future. You can enter prompts in any language - Korean, English, Japanese, Chinese, or any other language - and our AI will automatically translate and generate images based on your input.**"
-)
-
-
-# Function to boost prompt with LLM
-def boost_prompt(keyword):
-    if not keyword or keyword.strip() == "":
-        return "Please enter a keyword or theme first"
-    
-    if coh_client is None:
-        return "Cohere API token not set. Please set the COH_API environment variable."
-    
-    print(f"[INFO] Generating boosted prompt for keyword: {keyword}")
-    prompt = generate_prompts(keyword)
-    
-    # Final verification that we're only returning valid content
-    if isinstance(prompt, str) and len(prompt) > 10 and not prompt.startswith("Error") and not prompt.startswith("Failed"):
-        return prompt.strip()
-    else:
-        return "Failed to generate a suitable prompt. Please try again with a different keyword."
-
+    with gr.Column(elem_id="col-container"):
 
-with gr.Blocks(css=css, theme=gr.themes.Soft()) as demo:
-    gr.Markdown(
-        f"""
-        ## 🖌️ NSFW Uncensored Text & Imagery: AI Limits Explorer  
-        {author_note}
-        """, elem_classes=["title"]
-    )
-
-    with gr.Group(elem_classes="model-description"):
-        gr.HTML("""
-        <p>
-        <strong>Models Use cases: </strong><br>
-        </p>
-        <div style="display: flex; justify-content: center; align-items: center; gap: 10px; flex-wrap: wrap; margin-top: 10px; margin-bottom: 20px;">
- 
-            <a href="https://huggingface.co/spaces/Heartsync/FREE-NSFW-HUB" target="_blank">
-                <img src="https://img.shields.io/static/v1?label=huggingface&message=FREE%20NSFW%20HUB&color=%230000ff&labelColor=%23800080&logo=huggingface&logoColor=%23ffa500&style=for-the-badge" alt="badge">
-            </a>
-            <a href="https://huggingface.co/spaces/Heartsync/NSFW-Uncensored-Real" target="_blank">
-                <img src="https://img.shields.io/static/v1?label=Text%20to%20Image%28Real%29&message=NSFW%20Uncensored&color=%230000ff&labelColor=%23800080&logo=Huggingface&logoColor=%23ffa500&style=for-the-badge" alt="badge">
-            </a>            
-            <a href="https://huggingface.co/spaces/Heartsync/Novel-NSFW" target="_blank">
-                <img src="https://img.shields.io/static/v1?label=NOVEL%20GENERATOR&message=NSFW%20Uncensored&color=%23ffc0cb&labelColor=%23ffff00&logo=huggingface&logoColor=%23ffa500&style=for-the-badge" alt="badge">
-            </a>
-            <a href="https://huggingface.co/spaces/Heartsync/adult" target="_blank">
-                <img src="https://img.shields.io/static/v1?label=Text%20to%20Image%20to%20Video&message=ADULT&color=%23ff00ff&labelColor=%23000080&logo=Huggingface&logoColor=%23ffa500&style=for-the-badge" alt="badge">
-            </a>
-
-            <a href="https://huggingface.co/spaces/Heartsync/wan2-1-fast-security" target="_blank">
-                <img src="https://img.shields.io/static/v1?label=Image%20to%20Video&message=Wan%202.1%20I2V%20Fast&color=%23ffa500&labelColor=%23000080&logo=huggingface&logoColor=white&style=for-the-badge" alt="badge">
-            </a>
-        
-            <a href="https://huggingface.co/spaces/Heartsync/NSFW-Uncensored-video" target="_blank">
-                <img src="https://img.shields.io/static/v1?label=Image%20to%20Video&message=NSFW%20Uncensored&color=%230000ff&labelColor=%23800080&logo=Huggingface&logoColor=%23ffa500&style=for-the-badge" alt="badge">
-            </a>            
-            <a href="https://huggingface.co/spaces/Heartsync/NSFW-Uncensored-video2" target="_blank">
-                <img src="https://img.shields.io/static/v1?label=Image%20to%20Video(Mirror)&message=NSFW%20Uncensored&color=%230000ff&labelColor=%23800080&logo=Huggingface&logoColor=%23ffa500&style=for-the-badge" alt="badge">
-            </a>
-            <a href="https://huggingface.co/spaces/Heartsync/NSFW-Uncensored" target="_blank">
-                <img src="https://img.shields.io/static/v1?label=Text%20to%20Image%28Anime%29&message=NSFW%20Uncensored&color=%230000ff&labelColor=%23800080&logo=Huggingface&logoColor=%23ffa500&style=for-the-badge" alt="badge">
-            </a>
-            
-        </div>
-        <p>
-        <small style="opacity: 0.8;">High-quality image generation powered by StableDiffusionXL with video generation capability. Supports long prompts and various artistic styles.</small>
-        </p>
-        """)    
-    
-    # Create state variables to store the current image
-    current_image = gr.State(None)
-    current_seed = gr.State(0)
-
-    with gr.Column(elem_id="col-container", elem_classes=["container", "panel"]):
-        # Add keyword input and boost button
-        with gr.Row():
-            keyword_input = gr.Text(
-                label="Keyword Input",
-                show_label=True,
-                max_lines=1,
-                placeholder="Enter a keyword or theme in any language to generate an optimal prompt",
-                value="random",
-            )
-            boost_button = gr.Button("BOOST", elem_classes=["boost-btn"])
-            random_button = gr.Button("RANDOM", elem_classes=["random-btn"])
-        
         with gr.Row():
             prompt = gr.Text(
                 label="Prompt",
-                elem_id="prompt-box",
-                show_label=True,
-                max_lines=3,  # Increased to 3 lines (3x original)
-                placeholder="Enter your prompt in any language (Korean, English, Japanese, etc.)",
+                show_label=False,
+                max_lines=1,
+                placeholder="Enter your prompt (long prompts are automatically supported)",  # 変更: Updated placeholder
+                container=False,
             )
-            run_button = gr.Button("Generate", scale=0)
 
-        # Image output area
-        result = gr.Image(label="Generated Image", elem_classes=["gr-image"])
+            run_button = gr.Button("Run", scale=0)
+
+        result = gr.Image(label="Result", show_label=False)
+        
+        with gr.Accordion("Advanced Settings", open=False):
 
-        with gr.Accordion("Advanced Settings", open=False, elem_classes=["gr-accordion"]):
             negative_prompt = gr.Text(
                 label="Negative prompt",
                 max_lines=1,
-                placeholder="Enter a negative prompt in any language",
-                value="text, talk bubble, low quality, watermark, signature",
+                placeholder="Enter a negative prompt",
+                value="nsfw, (low quality, worst quality:1.2), very displeasing, 3d, watermark, signature, ugly, poorly drawn"
+            )
+
+            seed = gr.Slider(
+                label="Seed",
+                minimum=0,
+                maximum=MAX_SEED,
+                step=1,
+                value=0,
             )
 
-            seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
             randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
 
             with gr.Row():
-                width = gr.Slider(label="Width", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=1024)
-                height = gr.Slider(label="Height", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=1024)
+                width = gr.Slider(
+                    label="Width",
+                    minimum=256,
+                    maximum=MAX_IMAGE_SIZE,
+                    step=32,
+                    value=1024,
+                )
 
-            with gr.Row():
-                guidance_scale = gr.Slider(label="Guidance scale", minimum=0.0, maximum=20.0, step=0.1, value=7)
-                num_inference_steps = gr.Slider(label="Inference steps", minimum=1, maximum=28, step=1, value=28)
+                height = gr.Slider(
+                    label="Height",
+                    minimum=256,
+                    maximum=MAX_IMAGE_SIZE,
+                    step=32,
+                    value=1024,
+                )
 
-    # Define a function to store the generated image in state
-    def update_image_state(img, seed_val):
-        return img, seed_val
+            with gr.Row():
+                guidance_scale = gr.Slider(
+                    label="Guidance scale",
+                    minimum=0.0,
+                    maximum=20.0,
+                    step=0.1,
+                    value=7,
+                )
 
-    # Connect boost button to generate prompt
-    boost_button.click(
-        fn=boost_prompt,
-        inputs=[keyword_input],
-        outputs=[prompt]
-    )
-    
-    # Connect random button to insert random example
-    random_button.click(
-        fn=get_random_prompt,
-        inputs=[],
-        outputs=[prompt]
-    )
+                num_inference_steps = gr.Slider(
+                    label="Number of inference steps",
+                    minimum=1,
+                    maximum=28,
+                    step=1,
+                    value=28,
+                )
 
-    # Connect image generation button
     run_button.click(
         fn=infer,
-        inputs=[
-            prompt,
-            negative_prompt,
-            seed,
-            randomize_seed,
-            width,
-            height,
-            guidance_scale,
-            num_inference_steps,
-        ],
-        outputs=[result, current_seed]
-    ).then(
-        fn=update_image_state,
-        inputs=[result, current_seed],
-        outputs=[current_image, current_seed]
+        inputs=[prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
+        outputs=[result]
     )
 
-demo.queue().launch()
+demo.queue().launch()