import spaces import gradio as gr import torch from PIL import Image from diffusers import DiffusionPipeline import random from transformers import pipeline torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False torch.backends.cuda.matmul.allow_tf32 = True # 번역 모델 초기화 translator = pipeline("translation", model="Helsinki-NLP/opus-mt-ko-en") base_model = "black-forest-labs/FLUX.1-dev" pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=torch.bfloat16) lora_repo = "strangerzonehf/Flux-Xmas-Realpix-LoRA" trigger_word = "" pipe.load_lora_weights(lora_repo) pipe.to("cuda") MAX_SEED = 2**32-1 @spaces.GPU() def translate_and_generate(prompt, cfg_scale, steps, randomize_seed, seed, width, height, lora_scale, progress=gr.Progress(track_tqdm=True)): # 한글 감지 및 번역 def contains_korean(text): return any(ord('가') <= ord(char) <= ord('힣') for char in text) if contains_korean(prompt): # 한글을 영어로 번역 translated = translator(prompt)[0]['translation_text'] actual_prompt = translated else: actual_prompt = prompt if randomize_seed: seed = random.randint(0, MAX_SEED) generator = torch.Generator(device="cuda").manual_seed(seed) progress(0, "Starting image generation...") for i in range(1, steps + 1): if i % (steps // 10) == 0: progress(i / steps * 100, f"Processing step {i} of {steps}...") image = pipe( prompt=f"{actual_prompt} {trigger_word}", num_inference_steps=steps, guidance_scale=cfg_scale, width=width, height=height, generator=generator, joint_attention_kwargs={"scale": lora_scale}, ).images[0] progress(100, "Completed!") return image, seed example_image_path = "example0.webp" example_prompt = """Cozy winter scene with a Christmas atmosphere: a snow-covered cabin in the forest, warm light glowing from the windows, surrounded by sparkling Christmas decorations and a beautifully adorned Christmas tree. The sky is filled with stars, and soft snowflakes are gently falling, creating a serene and warm ambiance""" example_cfg_scale = 3.2 example_steps = 32 example_width = 1152 example_height = 896 example_seed = 3981632454 example_lora_scale = 0.85 def load_example(): example_image = Image.open(example_image_path) return example_prompt, example_cfg_scale, example_steps, True, example_seed, example_width, example_height, example_lora_scale, example_image css = """ .container { max-width: 1400px; margin: auto; padding: 20px; position: relative; background-image: url('file/example0.webp'); background-size: cover; background-position: center; min-height: 100vh; } .header { text-align: center; margin-bottom: 30px; color: white; text-shadow: 2px 2px 4px rgba(0,0,0,0.7); } .generate-btn { background-color: #2ecc71 !important; color: white !important; margin: 20px auto !important; display: block !important; width: 200px !important; } .generate-btn:hover { background-color: #27ae60 !important; } .parameter-box { background-color: rgba(245, 246, 250, 0.9); padding: 20px; border-radius: 10px; margin: 10px 0; } .result-box { background-color: rgba(245, 246, 250, 0.9); padding: 20px; border-radius: 10px; margin: 0 auto 20px auto; text-align: center; } .image-output { margin: 0 auto; display: block; max-width: 800px !important; } .accordion { margin-top: 20px; } .prompt-box { position: fixed; top: 20px; right: 20px; width: 300px; background-color: rgba(245, 246, 250, 0.9); padding: 20px; border-radius: 10px; z-index: 1000; } @keyframes snow { 0% { transform: translateY(0) translateX(0); } 100% { transform: translateY(100vh) translateX(100px); } } .snowflake { position: fixed; top: -10px; color: white; font-size: 20px; animation: snow 5s linear infinite; } """ js_code = """ function createSnowflake() { const snowflake = document.createElement('div'); snowflake.classList.add('snowflake'); snowflake.innerHTML = '❄'; snowflake.style.left = Math.random() * 100 + 'vw'; snowflake.style.animationDuration = Math.random() * 3 + 2 + 's'; snowflake.style.opacity = Math.random(); document.body.appendChild(snowflake); setTimeout(() => { snowflake.remove(); }, 5000); } setInterval(createSnowflake, 100); """ with gr.Blocks(css=css) as app: # JavaScript 눈 효과 gr.HTML(f"") # HTML5 오디오 요소 추가 gr.HTML(""" """) with gr.Column(elem_classes="container"): gr.Markdown("# 🎄 X-MAS LoRA", elem_classes="header") # 프롬프트 입력 박스를 별도로 배치 with gr.Group(elem_classes="prompt-box"): prompt = gr.TextArea( label="✍️ Your Prompt (한글 또는 영어)", placeholder="이미지를 설명하세요...", lines=5 ) generate_button = gr.Button( "🚀 Generate Image", elem_classes="generate-btn" ) # 이미지 출력 영역 with gr.Group(elem_classes="result-box"): gr.Markdown("### 🖼️ Generated Image") result = gr.Image(label="Result", elem_classes="image-output") # 옵션들을 아코디언으로 구성 with gr.Accordion("🎨 Advanced Options", open=False, elem_classes="accordion"): with gr.Group(elem_classes="parameter-box"): gr.Markdown("### 🎛️ Generation Parameters") with gr.Row(): with gr.Column(): cfg_scale = gr.Slider( label="CFG Scale", minimum=1, maximum=20, step=0.5, value=example_cfg_scale ) steps = gr.Slider( label="Steps", minimum=1, maximum=100, step=1, value=example_steps ) lora_scale = gr.Slider( label="LoRA Scale", minimum=0, maximum=1, step=0.01, value=example_lora_scale ) with gr.Group(elem_classes="parameter-box"): gr.Markdown("### 📐 Image Dimensions") with gr.Row(): width = gr.Slider( label="Width", minimum=256, maximum=1536, step=64, value=example_width ) height = gr.Slider( label="Height", minimum=256, maximum=1536, step=64, value=example_height ) with gr.Group(elem_classes="parameter-box"): gr.Markdown("### 🎲 Seed Settings") with gr.Row(): randomize_seed = gr.Checkbox( True, label="Randomize seed" ) seed = gr.Slider( label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=example_seed ) app.load( load_example, inputs=[], outputs=[prompt, cfg_scale, steps, randomize_seed, seed, width, height, lora_scale, result] ) generate_button.click( translate_and_generate, inputs=[prompt, cfg_scale, steps, randomize_seed, seed, width, height, lora_scale], outputs=[result, seed] ) app.queue() app.launch(js=js_code)