import spaces
import torch
from diffusers import FluxPipeline
import gradio as gr
import random
import numpy as np
import os
if torch.cuda.is_available():
device = "cuda"
print("GPU를 사용합니다")
else:
device = "cpu"
print("CPU를 사용합니다")
HF_TOKEN = os.getenv("HF_TOKEN")
MAX_SEED = np.iinfo(np.int32).max
CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv("CACHE_EXAMPLES", "0") == "1"
pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16)
pipe.to(device)
@spaces.GPU(duration=160)
def generate_image(prompt, num_inference_steps, height, width, guidance_scale, seed, num_images_per_prompt, progress=gr.Progress(track_tqdm=True)):
if seed is None or seed == 0:
seed = random.randint(1, MAX_SEED)
generator = torch.Generator().manual_seed(seed)
with torch.inference_mode():
output = pipe(
prompt=prompt,
num_inference_steps=num_inference_steps,
height=height,
width=width,
guidance_scale=guidance_scale,
generator=generator,
num_images_per_prompt=num_images_per_prompt
).images
return output
def random_seed():
return random.randint(1, MAX_SEED)
def create_random_seed():
new_seed = random_seed()
return [gr.Number.update(value=new_seed), f"현재 시드: {new_seed}"]
examples = [
["A cat holding a sign that says hello world"],
["a tiny astronaut hatching from an egg on the moon"],
["An astronaut on mars in a futuristic cyborg suit"],
]
css = """
.gradio-container {
max-width: 1400px !important;
margin: auto;
}
.image-container img {
max-height: 600px !important;
}
.image-slider {
height: 600px !important;
max-height: 600px !important;
}
h1 {
text-align: center;
font-family: 'Pretendard', sans-serif;
color: #EA580C;
font-size: 2.5rem;
font-weight: 700;
margin-bottom: 1.5rem;
text-shadow: 0 2px 4px rgba(0,0,0,0.1);
}
.subtitle {
text-align: center;
color: #4B5563;
font-size: 1.1rem;
margin-bottom: 2rem;
font-family: 'Pretendard', sans-serif;
}
.gr-button-primary {
background-color: #F97316 !important;
border: none !important;
box-shadow: 0 2px 4px rgba(234, 88, 12, 0.2) !important;
}
.gr-button-primary:hover {
background-color: #EA580C !important;
transform: translateY(-1px);
box-shadow: 0 4px 6px rgba(234, 88, 12, 0.25) !important;
}
.footer-content {
text-align: center;
margin-top: 3rem;
padding: 2rem;
background: linear-gradient(to bottom, #FFF7ED, white);
border-radius: 12px;
font-family: 'Pretendard', sans-serif;
}
.footer-content a {
color: #EA580C;
text-decoration: none;
font-weight: 500;
transition: all 0.2s;
}
.footer-content a:hover {
color: #C2410C;
}
.visit-button {
background-color: #EA580C;
color: white !important;
padding: 12px 24px;
border-radius: 8px;
font-weight: 600;
text-decoration: none;
display: inline-block;
transition: all 0.3s;
margin-top: 1rem;
box-shadow: 0 2px 4px rgba(234, 88, 12, 0.2);
font-size: 1.1rem;
}
.visit-button:hover {
background-color: #C2410C;
transform: translateY(-2px);
box-shadow: 0 4px 6px rgba(234, 88, 12, 0.25);
color: white !important;
}
.container-wrapper {
background: white;
border-radius: 16px;
padding: 2rem;
box-shadow: 0 4px 6px rgba(0, 0, 0, 0.05);
}
.image-container {
border-radius: 12px;
overflow: hidden;
border: 2px solid #F3F4F6;
}
.seed-button {
background-color: #F3F4F6 !important;
color: #374151 !important;
border: 1px solid #E5E7EB !important;
border-radius: 8px !important;
padding: 8px 16px !important;
font-size: 0.9rem !important;
font-weight: 500 !important;
transition: all 0.2s ease-in-out !important;
}
.seed-button:hover {
background-color: #E5E7EB !important;
transform: translateY(-1px);
box-shadow: 0 2px 4px rgba(0, 0, 0, 0.05);
}
.seed-text {
font-family: 'Pretendard', sans-serif;
color: #4B5563;
font-size: 0.9rem;
margin: 0;
line-height: 2.5;
font-weight: 500;
}
"""
with gr.Blocks(
theme=gr.themes.Soft(
primary_hue=gr.themes.Color(
c50="#FFF7ED",
c100="#FFEDD5",
c200="#FED7AA",
c300="#FDBA74",
c400="#FB923C",
c500="#F97316",
c600="#EA580C",
c700="#C2410C",
c800="#9A3412",
c900="#7C2D12",
c950="#431407",
),
secondary_hue="zinc",
neutral_hue="zinc",
font=("Pretendard", "sans-serif")
),
css=css
) as demo:
with gr.Row():
with gr.Column():
gr.HTML(
"""
끝장AI FLUX.1 이미지 생성기
강력한 AI 기술로 당신의 상상을 현실로 만들어보세요
"""
)
with gr.Group(elem_classes="container-wrapper"):
with gr.Column():
prompt = gr.Textbox(
label="프롬프트",
info="원하는 이미지를 설명해주세요",
placeholder="고양이..."
)
run_button = gr.Button("생성하기", variant="primary")
result = gr.Gallery(
label="생성된 AI 이미지",
elem_id="gallery",
elem_classes="image-container"
)
with gr.Accordion("고급 설정", open=False):
with gr.Row():
num_inference_steps = gr.Slider(
label="추론 단계 수",
info="이미지의 디노이징 단계 수입니다. 더 많은 단계는 더 높은 품질의 이미지를 생성하지만 시간이 더 걸립니다",
minimum=1,
maximum=50,
value=25,
step=1,
interactive=True,
show_label=True,
container=True,
randomize=False
)
guidance_scale = gr.Slider(
label="가이던스 스케일",
info="텍스트 프롬프트를 얼마나 충실히 따를지 제어합니다. 높은 값은 입력 텍스트에 더 가깝게 생성됩니다",
minimum=0.0,
maximum=7.0,
value=3.5,
step=0.1,
interactive=True,
show_label=True,
container=True,
randomize=False
)
with gr.Row():
width = gr.Slider(
label="너비",
info="이미지의 너비",
minimum=256,
maximum=1024,
step=32,
value=1024,
interactive=True,
show_label=True,
container=True,
randomize=False
)
height = gr.Slider(
label="높이",
info="이미지의 높이",
minimum=256,
maximum=1024,
step=32,
value=1024,
interactive=True,
show_label=True,
container=True,
randomize=False
)
with gr.Row():
seed = gr.Slider(
label="시드",
info="생성 프로세스를 시작하는 시작점입니다. 무작위는 0을 입력하세요",
value=42,
minimum=0,
maximum=MAX_SEED,
step=1
)
num_images_per_prompt = gr.Slider(
label="프롬프트당 이미지 수",
info="설정된 값으로 생성할 이미지의 수",
minimum=1,
maximum=4,
step=1,
value=2
)
gr.Examples(
examples=examples,
fn=generate_image,
inputs=[prompt, num_inference_steps, height, width, guidance_scale, seed, num_images_per_prompt],
outputs=[result],
cache_examples=CACHE_EXAMPLES
)
seed_button.click(
fn=create_random_seed,
outputs=[seed, seed_text]
)
gr.on(
triggers=[
prompt.submit,
run_button.click,
],
fn=generate_image,
inputs=[prompt, num_inference_steps, height, width, guidance_scale, seed, num_images_per_prompt],
outputs=[result],
)
gr.HTML(
"""
"""
)
demo.queue().launch(share=False)