Delete app-backup.py
Browse files- app-backup.py +0 -261
app-backup.py
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import gradio as gr
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import numpy as np
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import random
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import torch
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from diffusers import DiffusionPipeline
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import spaces
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# κΈ°λ³Έ μ€μ
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dtype = torch.bfloat16
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device = "cuda" if torch.cuda.is_available() else "cpu"
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# λͺ¨λΈ λ‘λ
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pipe = DiffusionPipeline.from_pretrained(
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"black-forest-labs/FLUX.1-schnell",
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torch_dtype=dtype
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).to(device)
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MAX_SEED = np.iinfo(np.int32).max
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MAX_IMAGE_SIZE = 2048
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# μ ν λμμΈ μ»¨μ
μμ
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EXAMPLES = [
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{
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"title": "Smart Coffee Machine",
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"prompt": """A sleek industrial design concept for a coffee machine:
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- Curved metallic body with minimal bezel
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- Touchscreen panel for settings
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- Modern matte black finish
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- Hand-drawn concept sketch style""",
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"width": 1024,
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"height": 1024
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},
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{
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"title": "AI Speaker",
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"prompt": """A futuristic AI speaker concept:
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- Cylindrical shape with LED ring near top
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- Voice assistant concept, floating panel controls
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- Smooth glossy finish with minimal seams
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- Techy, modern look in grayscale""",
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"width": 1024,
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"height": 1024
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},
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{
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"title": "Next-Gen Smartphone",
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"prompt": """A wireframe-style concept for a bezel-less smartphone:
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- Edge-to-edge display
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- Integrated camera under screen
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- Metallic frame, minimal ports
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- Sleek, glossy black design""",
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"width": 1024,
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"height": 1024
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},
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{
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"title": "Futuristic Electric Bicycle",
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"prompt": """An industrial design sketch of an electric bike:
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- Lightweight carbon frame, aerodynamic lines
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- Integrated battery, sleek display on handlebars
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- Neon color highlights on wheels
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- High-tech vibe, minimal clutter""",
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"width": 1024,
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"height": 1024
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},
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{
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"title": "Concept Car Interior",
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"prompt": """A luxurious and futuristic car interior concept:
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- Wrap-around digital dashboard
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- Minimalistic steering control, seat controls on touchscreen
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- Ambient LED accent lights
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- Soft leather seats, bright accent stitching""",
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"width": 1024,
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"height": 1024
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}
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]
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# Convert examples to Gradio format (if needed)
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GRADIO_EXAMPLES = [
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[example["prompt"], example["width"], example["height"]]
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for example in EXAMPLES
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]
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@spaces.GPU()
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def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, num_inference_steps=4, progress=gr.Progress(track_tqdm=True)):
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if randomize_seed:
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seed = random.randint(0, MAX_SEED)
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generator = torch.Generator().manual_seed(seed)
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image = pipe(
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prompt=prompt,
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width=width,
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height=height,
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num_inference_steps=num_inference_steps,
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generator=generator,
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guidance_scale=0.0
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).images[0]
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return image, seed
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# CSS μ€νμΌ (κΈ°μ‘΄ ꡬ쑰 μ μ§)
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css = """
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.container {
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display: flex;
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flex-direction: row;
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height: 100%;
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}
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.input-column {
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flex: 1;
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padding: 20px;
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border-right: 2px solid #eee;
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max-width: 800px;
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}
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.examples-column {
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flex: 1;
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padding: 20px;
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overflow-y: auto;
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background: #f7f7f7;
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}
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.title {
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text-align: center;
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color: #2a2a2a;
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padding: 20px;
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font-size: 2.5em;
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font-weight: bold;
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background: linear-gradient(90deg, #f0f0f0 0%, #ffffff 100%);
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border-bottom: 3px solid #ddd;
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margin-bottom: 30px;
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}
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.subtitle {
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text-align: center;
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color: #666;
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margin-bottom: 30px;
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}
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.input-box {
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background: white;
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padding: 20px;
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border-radius: 10px;
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box-shadow: 0 2px 10px rgba(0,0,0,0.1);
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margin-bottom: 20px;
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width: 100%;
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}
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.input-box textarea {
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width: 100% !important;
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min-width: 600px !important;
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font-size: 14px !important;
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line-height: 1.5 !important;
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padding: 12px !important;
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}
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.example-card {
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background: white;
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padding: 15px;
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margin: 10px 0;
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border-radius: 8px;
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box-shadow: 0 2px 5px rgba(0,0,0,0.05);
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}
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.example-title {
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font-weight: bold;
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color: #2a2a2a;
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margin-bottom: 10px;
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}
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.contain {
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max-width: 1400px !important;
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margin: 0 auto !important;
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}
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.input-area {
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flex: 2 !important;
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}
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.examples-area {
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flex: 1 !important;
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}
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"""
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with gr.Blocks(css=css) as demo:
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gr.Markdown(
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"""
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<div class="title">GINI Design</div>
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<div class="subtitle">Generate sleek industrial/product design concepts with FLUX AI</div>
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""")
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with gr.Row(equal_height=True):
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# μΌμͺ½ μ
λ ₯ 컬λΌ
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with gr.Column(elem_id="input-column", scale=2):
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with gr.Group(elem_classes="input-box"):
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prompt = gr.Text(
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label="Design Prompt",
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placeholder="Enter your product design concept details...",
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lines=10,
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elem_classes="prompt-input"
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)
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run_button = gr.Button("Generate Design", variant="primary")
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result = gr.Image(label="Generated Design")
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with gr.Accordion("Advanced Settings", open=False):
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seed = gr.Slider(
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label="Seed",
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minimum=0,
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maximum=MAX_SEED,
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step=1,
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value=0,
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)
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randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
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with gr.Row():
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width = gr.Slider(
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label="Width",
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minimum=256,
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maximum=MAX_IMAGE_SIZE,
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step=32,
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value=1024,
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)
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height = gr.Slider(
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label="Height",
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minimum=256,
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maximum=MAX_IMAGE_SIZE,
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step=32,
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value=1024,
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)
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num_inference_steps = gr.Slider(
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label="Number of inference steps",
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minimum=1,
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maximum=50,
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step=1,
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value=4,
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)
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# μ€λ₯Έμͺ½ μμ 컬λΌ
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with gr.Column(elem_id="examples-column", scale=1):
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gr.Markdown("### Example Product Designs")
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for example in EXAMPLES:
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with gr.Group(elem_classes="example-card"):
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gr.Markdown(f"#### {example['title']}")
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gr.Markdown(f"```\n{example['prompt']}\n```")
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def create_example_handler(ex):
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def handler():
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return {
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prompt: ex["prompt"],
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width: ex["width"],
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height: ex["height"]
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}
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return handler
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gr.Button("Use This Example", size="sm").click(
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fn=create_example_handler(example),
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outputs=[prompt, width, height]
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)
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# μ΄λ²€νΈ λ°μΈλ© (λ²νΌ ν΄λ¦ & ν
μ€νΈλ°μ€ μν°)
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gr.on(
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triggers=[run_button.click, prompt.submit],
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fn=infer,
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inputs=[prompt, seed, randomize_seed, width, height, num_inference_steps],
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outputs=[result, seed]
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)
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if __name__ == "__main__":
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demo.queue()
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demo.launch(
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server_name="0.0.0.0",
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server_port=7860,
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share=False,
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show_error=True,
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debug=True
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)
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