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import gradio as gr
import numpy as np
import random
import torch
from diffusers import DiffusionPipeline
import spaces
from transformers import pipeline
# ๊ธฐ๋ณธ ์ค์
dtype = torch.bfloat16
device = "cuda" if torch.cuda.is_available() else "cpu"
# ํ๊ตญ์ด-์์ด ๋ฒ์ญ ๋ชจ๋ธ ๋ก๋ (CPU์์ ์คํ)
translator = pipeline("translation", model="Helsinki-NLP/opus-mt-ko-en", device="cpu")
# ๋ชจ๋ธ ๋ก๋
pipe = DiffusionPipeline.from_pretrained(
"black-forest-labs/FLUX.1-schnell",
torch_dtype=dtype
).to(device)
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 2048
# ์ ํ ๋์์ธ ์ปจ์
์์
EXAMPLES = [
{
"title": "Smart Coffee Machine",
"prompt": """A sleek industrial design concept for a coffee machine:
- Curved metallic body with minimal bezel
- Touchscreen panel for settings
- Modern matte black finish
- Hand-drawn concept sketch style""",
"width": 1024,
"height": 1024
},
{
"title": "AI Speaker",
"prompt": """A futuristic AI speaker concept:
- Cylindrical shape with LED ring near top
- Voice assistant concept, floating panel controls
- Smooth glossy finish with minimal seams
- Techy, modern look in grayscale""",
"width": 1024,
"height": 1024
},
{
"title": "Next-Gen Smartphone",
"prompt": """A wireframe-style concept for a bezel-less smartphone:
- Edge-to-edge display
- Integrated camera under screen
- Metallic frame, minimal ports
- Sleek, glossy black design""",
"width": 1024,
"height": 1024
},
{
"title": "Futuristic Electric Bicycle",
"prompt": """An industrial design sketch of an electric bike:
- Lightweight carbon frame, aerodynamic lines
- Integrated battery, sleek display on handlebars
- Neon color highlights on wheels
- High-tech vibe, minimal clutter""",
"width": 1024,
"height": 1024
},
{
"title": "Concept Car Interior",
"prompt": """A luxurious and futuristic car interior concept:
- Wrap-around digital dashboard
- Minimalistic steering control, seat controls on touchscreen
- Ambient LED accent lights
- Soft leather seats, bright accent stitching""",
"width": 1024,
"height": 1024
}
]
# Convert examples to Gradio format (if needed)
GRADIO_EXAMPLES = [
[example["prompt"], example["width"], example["height"]]
for example in EXAMPLES
]
# ํ๊ตญ์ด ๊ฐ์ง ํจ์
def contains_korean(text):
for char in text:
if ord('๊ฐ') <= ord(char) <= ord('ํฃ'):
return True
return False
# ํ์์ ๋ฒ์ญ ํ ์ถ๋ก ํจ์
@spaces.GPU()
def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, num_inference_steps=4, progress=gr.Progress(track_tqdm=True)):
# ํ๊ตญ์ด ๊ฐ์ง ๋ฐ ๋ฒ์ญ
original_prompt = prompt
translated = False
if contains_korean(prompt):
translated = True
translation = translator(prompt)
prompt = translation[0]['translation_text']
# ๋๋ค ์๋ ์ค์
if randomize_seed:
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)
# ๋ชจ๋ธ ์คํ
image = pipe(
prompt=prompt,
width=width,
height=height,
num_inference_steps=num_inference_steps,
generator=generator,
guidance_scale=0.0
).images[0]
# ๋ฒ์ญ ์ ๋ณด ๋ฐํ
if translated:
return image, seed, original_prompt, prompt
else:
return image, seed, None, None
# CSS ์คํ์ผ (๊ธฐ์กด ๊ตฌ์กฐ ์ ์ง)
css = """
.container {
display: flex;
flex-direction: row;
height: 100%;
}
.input-column {
flex: 1;
padding: 20px;
border-right: 2px solid #eee;
max-width: 800px;
}
.examples-column {
flex: 1;
padding: 20px;
overflow-y: auto;
background: #f7f7f7;
}
.title {
text-align: center;
color: #2a2a2a;
padding: 20px;
font-size: 2.5em;
font-weight: bold;
background: linear-gradient(90deg, #f0f0f0 0%, #ffffff 100%);
border-bottom: 3px solid #ddd;
margin-bottom: 30px;
}
.subtitle {
text-align: center;
color: #666;
margin-bottom: 30px;
}
.input-box {
background: white;
padding: 20px;
border-radius: 10px;
box-shadow: 0 2px 10px rgba(0,0,0,0.1);
margin-bottom: 20px;
width: 100%;
}
.input-box textarea {
width: 100% !important;
min-width: 600px !important;
font-size: 14px !important;
line-height: 1.5 !important;
padding: 12px !important;
}
.example-card {
background: white;
padding: 15px;
margin: 10px 0;
border-radius: 8px;
box-shadow: 0 2px 5px rgba(0,0,0,0.05);
}
.example-title {
font-weight: bold;
color: #2a2a2a;
margin-bottom: 10px;
}
.contain {
max-width: 1400px !important;
margin: 0 auto !important;
}
.input-area {
flex: 2 !important;
}
.examples-area {
flex: 1 !important;
}
.translation-info {
background-color: #f8f9fa;
border-left: 4px solid #17a2b8;
padding: 10px 15px;
margin-top: 10px;
border-radius: 4px;
font-size: 14px;
}
"""
with gr.Blocks(css=css) as demo:
gr.Markdown(
"""
<div class="title">GINI Design</div>
<div class="subtitle">Generate sleek industrial/product design concepts with FLUX AI</div>
""")
with gr.Row(equal_height=True):
# ์ผ์ชฝ ์
๋ ฅ ์ปฌ๋ผ
with gr.Column(elem_id="input-column", scale=2):
with gr.Group(elem_classes="input-box"):
prompt = gr.Text(
label="Design Prompt (ํ๊ตญ์ด ๋๋ ์์ด๋ก ์
๋ ฅํ์ธ์)",
placeholder="Enter your product design concept details in Korean or English...",
lines=10,
elem_classes="prompt-input"
)
run_button = gr.Button("Generate Design", variant="primary")
result = gr.Image(label="Generated Design")
# ๋ฒ์ญ ์ ๋ณด ํ์ ์์ญ
original_prompt = gr.Textbox(visible=False)
translated_prompt = gr.Textbox(visible=False)
translation_info = gr.Markdown(visible=False, elem_classes="translation-info")
# ๋ฒ์ญ ์ ๋ณด ์
๋ฐ์ดํธ ํจ์
def update_translation_info(original, translated):
if original and translated:
return gr.update(visible=True, value=f"๐ Korean prompt was translated to English:\n\n**Original:** {original}\n\n**Translated:** {translated}")
else:
return gr.update(visible=False)
with gr.Accordion("Advanced Settings", open=False):
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,
)
num_inference_steps = gr.Slider(
label="Number of inference steps",
minimum=1,
maximum=50,
step=1,
value=4,
)
# ์ค๋ฅธ์ชฝ ์์ ์ปฌ๋ผ
with gr.Column(elem_id="examples-column", scale=1):
gr.Markdown("### Example Product Designs")
for example in EXAMPLES:
with gr.Group(elem_classes="example-card"):
gr.Markdown(f"#### {example['title']}")
gr.Markdown(f"```\n{example['prompt']}\n```")
def create_example_handler(ex):
def handler():
return {
prompt: ex["prompt"],
width: ex["width"],
height: ex["height"]
}
return handler
gr.Button("Use This Example", size="sm").click(
fn=create_example_handler(example),
outputs=[prompt, width, height]
)
# ์ด๋ฒคํธ ๋ฐ์ธ๋ฉ (๋ฒํผ ํด๋ฆญ & ํ
์คํธ๋ฐ์ค ์ํฐ)
gr.on(
triggers=[run_button.click, prompt.submit],
fn=infer,
inputs=[prompt, seed, randomize_seed, width, height, num_inference_steps],
outputs=[result, seed, original_prompt, translated_prompt]
)
# ๋ฒ์ญ ์ ๋ณด ์
๋ฐ์ดํธ ์ด๋ฒคํธ
gr.on(
triggers=[original_prompt.change, translated_prompt.change],
fn=update_translation_info,
inputs=[original_prompt, translated_prompt],
outputs=[translation_info]
)
if __name__ == "__main__":
demo.queue()
demo.launch(
server_name="0.0.0.0",
server_port=7860,
share=False,
show_error=True,
debug=True
) |