import os import gc import uuid import random import tempfile import time from datetime import datetime from typing import Any from huggingface_hub import login, hf_hub_download import spaces import gradio as gr import numpy as np import torch from PIL import Image, ImageDraw, ImageFont from diffusers import FluxPipeline from transformers import pipeline # 메모리 정리 함수 def clear_memory(): gc.collect() try: if torch.cuda.is_available(): with torch.cuda.device(0): torch.cuda.empty_cache() except: pass # GPU 설정 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") if torch.cuda.is_available(): try: with torch.cuda.device(0): torch.cuda.empty_cache() torch.backends.cudnn.benchmark = True torch.backends.cuda.matmul.allow_tf32 = True except: print("Warning: Could not configure CUDA settings") # HF 토큰 설정 HF_TOKEN = os.getenv("HF_TOKEN") if HF_TOKEN is None: raise ValueError("Please set the HF_TOKEN environment variable") try: login(token=HF_TOKEN) except Exception as e: raise ValueError(f"Failed to login to Hugging Face: {str(e)}") translator = pipeline("translation", model="Helsinki-NLP/opus-mt-ko-en", device=-1) # CPU에서 실행 def translate_to_english(text: str) -> str: """한글 텍스트를 영어로 번역""" try: if any(ord('가') <= ord(char) <= ord('힣') for char in text): translated = translator(text, max_length=128)[0]['translation_text'] print(f"Translated '{text}' to '{translated}'") return translated return text except Exception as e: print(f"Translation error: {str(e)}") return text # FLUX 파이프라인 초기화 부분 수정 print("Initializing FLUX pipeline...") try: pipe = FluxPipeline.from_pretrained( "black-forest-labs/FLUX.1-dev", torch_dtype=torch.float16, use_auth_token=HF_TOKEN ) print("FLUX pipeline initialized successfully") # 메모리 최적화 설정 pipe.enable_attention_slicing(slice_size=1) # GPU 설정 if torch.cuda.is_available(): pipe = pipe.to("cuda:0") torch.cuda.empty_cache() torch.backends.cudnn.benchmark = True torch.backends.cuda.matmul.allow_tf32 = True print("Pipeline optimization settings applied") except Exception as e: print(f"Error initializing FLUX pipeline: {str(e)}") raise # LoRA 가중치 로드 부분 수정 print("Loading LoRA weights...") try: # 로컬 LoRA 파일의 절대 경로 확인 current_dir = os.path.dirname(os.path.abspath(__file__)) lora_path = os.path.join(current_dir, "myt-flux-fantasy.safetensors") if not os.path.exists(lora_path): raise FileNotFoundError(f"LoRA file not found at: {lora_path}") print(f"Loading LoRA weights from: {lora_path}") # LoRA 가중치 로드 pipe.load_lora_weights(lora_path) pipe.fuse_lora(lora_scale=0.75) # lora_scale 값 조정 # 메모리 정리 torch.cuda.empty_cache() gc.collect() print("LoRA weights loaded and fused successfully") print(f"Current device: {pipe.device}") except Exception as e: print(f"Error loading LoRA weights: {str(e)}") print(f"Full error details: {repr(e)}") raise ValueError(f"Failed to load LoRA weights: {str(e)}") @spaces.GPU(duration=60) def generate_image( prompt: str, seed: int, randomize_seed: bool, width: int, height: int, guidance_scale: float, num_inference_steps: int, progress: gr.Progress = gr.Progress() ): try: clear_memory() translated_prompt = translate_to_english(prompt) print(f"Processing prompt: {translated_prompt}") if randomize_seed: seed = random.randint(0, MAX_SEED) generator = torch.Generator(device=device).manual_seed(seed) print(f"Current device: {pipe.device}") print(f"Starting image generation...") with torch.inference_mode(), torch.cuda.amp.autocast(enabled=True): image = pipe( prompt=translated_prompt, width=width, height=height, num_inference_steps=num_inference_steps, guidance_scale=guidance_scale, generator=generator, num_images_per_prompt=1, ).images[0] filepath = save_generated_image(image, translated_prompt) print(f"Image generated and saved to: {filepath}") return image, seed except Exception as e: print(f"Generation error: {str(e)}") print(f"Full error details: {repr(e)}") raise gr.Error(f"Image generation failed: {str(e)}") finally: clear_memory() # 저장 디렉토리 설정 SAVE_DIR = "saved_images" if not os.path.exists(SAVE_DIR): os.makedirs(SAVE_DIR, exist_ok=True) MAX_SEED = np.iinfo(np.int32).max MAX_IMAGE_SIZE = 1024 def save_generated_image(image, prompt): timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") unique_id = str(uuid.uuid4())[:8] filename = f"{timestamp}_{unique_id}.png" filepath = os.path.join(SAVE_DIR, filename) image.save(filepath) return filepath def add_text_with_stroke(draw, text, x, y, font, text_color, stroke_width): """텍스트에 외곽선을 추가하는 함수""" for adj_x in range(-stroke_width, stroke_width + 1): for adj_y in range(-stroke_width, stroke_width + 1): draw.text((x + adj_x, y + adj_y), text, font=font, fill=text_color) def add_text_to_image( input_image, text, font_size, color, opacity, x_position, y_position, thickness, text_position_type, font_choice ): try: if input_image is None or text.strip() == "": return input_image if not isinstance(input_image, Image.Image): if isinstance(input_image, np.ndarray): image = Image.fromarray(input_image) else: raise ValueError("Unsupported image type") else: image = input_image.copy() if image.mode != 'RGBA': image = image.convert('RGBA') font_files = { "Default": "DejaVuSans.ttf", "Korean Regular": "ko-Regular.ttf" } try: font_file = font_files.get(font_choice, "DejaVuSans.ttf") font = ImageFont.truetype(font_file, int(font_size)) except Exception as e: print(f"Font loading error ({font_choice}): {str(e)}") font = ImageFont.load_default() color_map = { 'White': (255, 255, 255), 'Black': (0, 0, 0), 'Red': (255, 0, 0), 'Green': (0, 255, 0), 'Blue': (0, 0, 255), 'Yellow': (255, 255, 0), 'Purple': (128, 0, 128) } rgb_color = color_map.get(color, (255, 255, 255)) temp_draw = ImageDraw.Draw(image) text_bbox = temp_draw.textbbox((0, 0), text, font=font) text_width = text_bbox[2] - text_bbox[0] text_height = text_bbox[3] - text_bbox[1] actual_x = int((image.width - text_width) * (x_position / 100)) actual_y = int((image.height - text_height) * (y_position / 100)) text_color = (*rgb_color, int(opacity)) txt_overlay = Image.new('RGBA', image.size, (255, 255, 255, 0)) draw = ImageDraw.Draw(txt_overlay) add_text_with_stroke( draw, text, actual_x, actual_y, font, text_color, int(thickness) ) output_image = Image.alpha_composite(image, txt_overlay) output_image = output_image.convert('RGB') return output_image except Exception as e: print(f"Error in add_text_to_image: {str(e)}") return input_image css = """ footer {display: none} .main-title { text-align: center; margin: 1em 0; padding: 1.5em; background: linear-gradient(135deg, #f5f7fa 0%, #c3cfe2 100%); border-radius: 15px; box-shadow: 0 4px 6px rgba(0,0,0,0.1); } .main-title h1 { color: #2196F3; font-size: 2.8em; margin-bottom: 0.3em; font-weight: 700; } .main-title p { color: #555; font-size: 1.3em; line-height: 1.4; } .container { max-width: 1200px; margin: auto; padding: 20px; } .input-panel, .output-panel { background: white; padding: 1.5em; border-radius: 12px; box-shadow: 0 2px 8px rgba(0,0,0,0.08); margin-bottom: 1em; } """ import requests def enhance_prompt(prompt: str) -> str: """프롬프트를 애니메이션 스타일로 증강""" try: # 기본 품질 향상 프롬프트 추가 enhancements = [ "masterpiece, best quality, highly detailed", "anime style, animation style", "vibrant colors, perfect lighting", "professional composition", "dynamic pose, expressive features", "detailed background, perfect shadows", "[trigger]" ] # 애니메이션 스타일 프롬프트 변환 anime_style_prompt = f"an animated {prompt}, detailed anime art style" # 최종 프롬프트 구성 final_prompt = f"{anime_style_prompt}, {', '.join(enhancements)}" print(f"Enhanced prompt: {final_prompt}") return final_prompt except Exception as e: print(f"Prompt enhancement failed: {str(e)}") return prompt # 기존의 pipeline 초기화 부분 제거 # try: # prompt_enhancer = pipeline(...) # except Exception as e: # print(f"Error initializing prompt enhancer: {str(e)}") # prompt_enhancer = None with gr.Blocks(theme=gr.themes.Soft(), css=css) as demo: gr.HTML("""

🎨 Webtoon Studio

Generate webtoon-style images and add text with various styles and positions.

""") with gr.Row(): with gr.Column(scale=1): gen_prompt = gr.Textbox( label="Generation Prompt", placeholder="Enter your image generation prompt..." ) enhance_btn = gr.Button("✨ Enhance Prompt", variant="secondary") with gr.Row(): gen_width = gr.Slider(512, 1024, 768, step=64, label="Width") gen_height = gr.Slider(512, 1024, 768, step=64, label="Height") with gr.Row(): guidance_scale = gr.Slider(1, 20, 7.5, step=0.5, label="Guidance Scale") num_steps = gr.Slider(1, 50, 30, step=1, label="Number of Steps") with gr.Row(): seed = gr.Number(label="Seed", value=-1) randomize_seed = gr.Checkbox(label="Randomize Seed", value=True) generate_btn = gr.Button("Generate Image", variant="primary") output_image = gr.Image( label="Generated Image", type="pil", show_download_button=True ) output_seed = gr.Number(label="Used Seed", interactive=False) # 텍스트 추가 섹션 with gr.Accordion("Text Options", open=False): text_input = gr.Textbox( label="Text Content", placeholder="Enter text to add..." ) text_position_type = gr.Radio( choices=["Text Over Image"], value="Text Over Image", label="Text Position", visible=True ) with gr.Row(): font_choice = gr.Dropdown( choices=["Default", "Korean Regular"], value="Default", label="Font Selection", interactive=True ) font_size = gr.Slider( minimum=10, maximum=200, value=40, step=5, label="Font Size" ) with gr.Row(): color_dropdown = gr.Dropdown( choices=["White", "Black", "Red", "Green", "Blue", "Yellow", "Purple"], value="White", label="Text Color" ) thickness = gr.Slider( minimum=0, maximum=10, value=1, step=1, label="Text Thickness" ) with gr.Row(): opacity_slider = gr.Slider( minimum=0, maximum=255, value=255, step=1, label="Opacity" ) with gr.Row(): x_position = gr.Slider( minimum=0, maximum=100, value=50, step=1, label="Left(0%)~Right(100%)" ) y_position = gr.Slider( minimum=0, maximum=100, value=50, step=1, label="High(0%)~Low(100%)" ) add_text_btn = gr.Button("Apply Text", variant="primary") # 이벤트 바인딩 generate_btn.click( fn=generate_image, inputs=[ gen_prompt, seed, randomize_seed, gen_width, gen_height, guidance_scale, num_steps, ], outputs=[output_image, output_seed] ) add_text_btn.click( fn=add_text_to_image, inputs=[ output_image, text_input, font_size, color_dropdown, opacity_slider, x_position, y_position, thickness, text_position_type, font_choice ], outputs=output_image ) # 이벤트 바인딩 추가 def update_prompt(prompt): enhanced = enhance_prompt(prompt) return enhanced enhance_btn.click( fn=update_prompt, inputs=[gen_prompt], outputs=[gen_prompt] ) demo.queue(max_size=5) demo.launch( server_name="0.0.0.0", server_port=7860, share=False, max_threads=2 )