Spaces:
Running
on
Zero
Running
on
Zero
File size: 5,130 Bytes
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import gradio as gr
import numpy as np
import random
import spaces
import torch
from diffusers import DiffusionPipeline
from transformers import pipeline
# Translation pipeline and hardware settings
device = "cuda" if torch.cuda.is_available() else "cpu"
translator = pipeline("translation", model="Helsinki-NLP/opus-mt-ko-en", device=device)
dtype = torch.bfloat16
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
@spaces.GPU()
def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, num_inference_steps=4, progress=gr.Progress(track_tqdm=True)):
if randomize_seed:
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)
# Korean input detection and translation
if any('\uAC00' <= char <= '\uD7A3' for char in prompt):
print("Translating Korean prompt...")
translated_prompt = translator(prompt, max_length=512)[0]['translation_text']
print("Translated prompt:", translated_prompt)
prompt = translated_prompt
image = pipe(
prompt = prompt,
width = width,
height = height,
num_inference_steps = num_inference_steps,
generator = generator,
guidance_scale=0.0
).images[0]
return image, seed
examples = [
["[ํ๊ธ] [์คํ์ผ: ๋ชจ๋] [์์: ๋นจ๊ฐ๊ณผ ๊ฒ์ ] [์ปจ์
: ์๋น] [ํ
์คํธ: '๋ง์๋์ง'] [๋ฐฐ๊ฒฝ: ์ฌํ]"],
["[Style: Corporate] [Color: Navy and Silver] [Concept: Finance] [Text: 'TRUST'] [Background: Professional]"],
["[Style: Dynamic] [Color: Purple and Orange] [Concept: Creative Agency] [Text: 'SPARK'] [Background: Abstract]"],
["[Style: Minimalist] [Color: Red and White] [Concept: Sports] [Text: 'POWER'] [Background: Clean]"]
]
css = """
footer {visibility: hidden}
.container {max-width: 850px; margin: auto; padding: 20px}
.title {text-align: center; margin-bottom: 20px}
#prompt {min-height: 50px}
#result {min-height: 400px}
.gr-box {border-radius: 10px; border: 1px solid #ddd}
"""
def create_snow_effect():
# CSS ์คํ์ผ ์ ์
snow_css = """
@keyframes snowfall {
0% {
transform: translateY(-10vh) translateX(0);
opacity: 1;
}
100% {
transform: translateY(100vh) translateX(100px);
opacity: 0.3;
}
}
.snowflake {
position: fixed;
color: white;
font-size: 1.5em;
user-select: none;
z-index: 1000;
pointer-events: none;
animation: snowfall linear infinite;
}
"""
# JavaScript ์ฝ๋ ์ ์
snow_js = """
function createSnowflake() {
const snowflake = document.createElement('div');
snowflake.innerHTML = 'โ';
snowflake.className = 'snowflake';
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, 200);
"""
# CSS์ JavaScript๋ฅผ ๊ฒฐํฉํ HTML
snow_html = f"""
<style>
{snow_css}
</style>
<script>
{snow_js}
</script>
"""
return gr.HTML(snow_html)
with gr.Blocks(theme="Yntec/HaleyCH_Theme_Orange", css=css) as demo:
create_snow_effect()
gr.HTML("<h1 class='title'>LOGO Generator AI</h1>")
with gr.Column(elem_id="container"):
with gr.Group():
prompt = gr.Text(
label="PROMPT",
placeholder="Text input Prompt (Korean input supported)",
lines=2
)
run_button = gr.Button("Generate Logo", variant="primary")
with gr.Row():
result = gr.Image(label="Generated Logo", show_label=True)
with gr.Accordion("Advanced Settings", open=False):
with gr.Row():
seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
randomize_seed = gr.Checkbox(label="Random Seed", value=True)
with gr.Row():
width = gr.Slider(label="Width", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=512)
height = gr.Slider(label="Height", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=512)
num_inference_steps = gr.Slider(label="Quality", minimum=1, maximum=50, step=1, value=4)
gr.Examples(
examples=examples,
fn=infer,
inputs=[prompt],
outputs=[result, seed],
cache_examples="lazy"
)
gr.on(
triggers=[run_button.click, prompt.submit],
fn=infer,
inputs=[prompt, seed, randomize_seed, width, height, num_inference_steps],
outputs=[result, seed]
)
demo.launch() |