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)