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Runtime error
Runtime error
Update app-backup.py
Browse files- app-backup.py +103 -53
app-backup.py
CHANGED
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@@ -12,6 +12,7 @@ import torch
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from diffusers import FluxPipeline
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from PIL import Image
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from transformers import pipeline
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translator = pipeline("translation", model="Helsinki-NLP/opus-mt-ko-en")
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@@ -35,6 +36,33 @@ torch.backends.cuda.matmul.allow_tf32 = True
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if not path.exists(gallery_path):
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os.makedirs(gallery_path, exist_ok=True)
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class timer:
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def __init__(self, method_name="timed process"):
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self.method = method_name
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@@ -49,11 +77,10 @@ class timer:
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if not path.exists(cache_path):
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os.makedirs(cache_path, exist_ok=True)
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-
# ์ธ์ฆ๋ ๋ชจ๋ธ ๋ก๋
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pipe = FluxPipeline.from_pretrained(
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"black-forest-labs/FLUX.1-dev",
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torch_dtype=torch.bfloat16,
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use_auth_token=HF_TOKEN
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)
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# Hyper-SD LoRA ๋ก๋
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@@ -98,26 +125,15 @@ def save_image(image):
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print(f"Error in save_image: {str(e)}")
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return None
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-
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["A 3D Star Wars Darth Vader helmet, highly detailed metallic finish"],
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["A 3D Iron Man mask with glowing eyes and metallic red-gold finish"],
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["A detailed 3D Pokemon Pikachu figure with glossy surface"],
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["A 3D geometric abstract cube transforming into a sphere, metallic finish"],
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["A 3D steampunk mechanical heart with brass and copper details"],
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["A 3D crystal dragon with transparent iridescent scales"],
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["A 3D futuristic hovering drone with neon light accents"],
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["A 3D ancient Greek warrior helmet with ornate details"],
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["A 3D robotic butterfly with mechanical wings and metallic finish"],
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["A 3D floating magical crystal orb with internal energy swirls"]
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]
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@spaces.GPU
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def process_and_save_image(height=1024, width=1024, steps=8, scales=3.5, prompt="", seed=None):
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global pipe
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if seed is None:
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seed =
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# ํ๊ธ ๊ฐ์ง ๋ฐ ๋ฒ์ญ
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def contains_korean(text):
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@@ -128,7 +144,7 @@ def process_and_save_image(height=1024, width=1024, steps=8, scales=3.5, prompt=
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translated = translator(prompt)[0]['translation_text']
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prompt = translated
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formatted_prompt = f"wbgmsst, 3D, {prompt}
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with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16), timer("inference"):
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try:
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@@ -151,21 +167,10 @@ def process_and_save_image(height=1024, width=1024, steps=8, scales=3.5, prompt=
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print(f"Error in image generation: {str(e)}")
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return None
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def
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def process_example(prompt):
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return process_and_save_image(
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height=1024,
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width=1024,
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steps=8,
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scales=3.5,
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prompt=prompt,
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seed=get_random_seed()
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)
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-
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# Gradio ์ธํฐํ์ด์ค
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with gr.Blocks(
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theme=gr.themes.Soft(),
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@@ -190,13 +195,27 @@ with gr.Blocks(
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max-width: 1024px;
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margin: auto;
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}
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"""
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) as demo:
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gr.HTML(
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"""
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<div style="text-align: center; max-width: 800px; margin: 0 auto; padding: 20px;">
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<h1 style="font-size: 2.5rem; color: #2196F3;">3D Style Image Generator</h1>
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<p style="font-size: 1.2rem; color: #666;">Create amazing 3D-style images with AI</p>
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</div>
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"""
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)
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@@ -263,33 +282,64 @@ with gr.Blocks(
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value="3d.webp"
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)
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#
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gr.
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# ์ด๋ฒคํธ ํธ๋ค๋ฌ
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generate_btn.click(
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process_and_save_image,
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inputs=[height, width, steps, scales, prompt, seed],
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outputs=output
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).then(
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update_seed,
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outputs=[seed]
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)
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randomize_seed.click(
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)
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if __name__ == "__main__":
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demo.launch(allowed_paths=[PERSISTENT_DIR])
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from diffusers import FluxPipeline
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from PIL import Image
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from transformers import pipeline
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import base64
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translator = pipeline("translation", model="Helsinki-NLP/opus-mt-ko-en")
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if not path.exists(gallery_path):
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os.makedirs(gallery_path, exist_ok=True)
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# ์ํ ์ด๋ฏธ์ง์ ํ๋กฌํํธ ์ ์
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SAMPLE_IMAGES = {
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"3d2.webp": "the most famous hero according to Yuri Milner",
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"3d3.webp": "purple nest",
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"3d4.webp": "Timothy's sabbath",
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"3d5.webp": "A schoolboy friend of Juliรกn Carax, fun-loving and loyal",
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"3d6.webp": "Friend of Daniel and his father",
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"3d7.webp": "WHERE ships of purple gently toss On seas of daffodil",
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"3d8.webp": "Beat the drums of tragedy for me, And let the white violins whir thin and slow",
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"3d9.webp": "And let the choir sing a stormy song To drown the rattle of my dying breath.",
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"3d10.webp": "Beat the drums of tragedy and death",
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"3d11.webp": "Beat the drums of tragedy for me.",
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"3d12.webp": "Touching the infinite, else far and untrod, With oracles divine that speak of God.",
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"3d13.webp": "Night, standing on her starry pulpit, free, Utters them in the dread, the silver roll Of spheres, woods, winds and waves, alternately",
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"3d14.webp": "On sermons deep, fit time to feast the soul.",
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"3d15.webp": "The bee is cradled in the bud; and far, Cold glittering lights, the azure curtain, throngโ Planet on beaming planet, star on star.",
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"3d16.webp": "The lark's sweet pipe has ceased its latest song",
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"3d17.webp": "the snake was a roaming dog",
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"3d18.webp": "Antonio Battistella portraying Father of Giulia",
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"3d19.webp": "So straight to her father the brisk young lady went, And said, grant me one favour, do give your consent",
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"3d20.webp": "Before that we are marryโd let me your father see, All fear is, now miscarryโd, my heart is full of glee",
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"3d21.webp": "My heart you now have gained, you are all I prize, So make yourself contented, pray be satisfied.",
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"3d22.webp": "O pray what is the favour that of me you crave? If it lies in my power you the same shall have",
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"3d23.webp": "Could I but see your father, and my mind reveal, I have both gold and silver, and houses at my will",
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"3d1.webp": "the most famous hero according to Zhou Qi"
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}
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class timer:
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def __init__(self, method_name="timed process"):
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self.method = method_name
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if not path.exists(cache_path):
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os.makedirs(cache_path, exist_ok=True)
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pipe = FluxPipeline.from_pretrained(
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"black-forest-labs/FLUX.1-dev",
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torch_dtype=torch.bfloat16,
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use_auth_token=HF_TOKEN # ๊ฒฝ๊ณ ๋ฉ์์ง๊ฐ ๋จ์ง๋ง ๋ฌด์ ๊ฐ๋ฅ
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)
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# Hyper-SD LoRA ๋ก๋
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print(f"Error in save_image: {str(e)}")
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return None
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def get_random_seed():
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return torch.randint(0, 1000000, (1,)).item()
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@spaces.GPU
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def process_and_save_image(height=1024, width=1024, steps=8, scales=3.5, prompt="", seed=None):
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global pipe
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if seed is None:
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seed = get_random_seed()
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# ํ๊ธ ๊ฐ์ง ๋ฐ ๋ฒ์ญ
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def contains_korean(text):
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translated = translator(prompt)[0]['translation_text']
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prompt = translated
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formatted_prompt = f"wbgmsst, 3D, {prompt}, white background"
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with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16), timer("inference"):
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try:
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print(f"Error in image generation: {str(e)}")
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return None
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def update_random_seed():
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"""๋ฒํผ์ผ๋ก ๋๋ ์ ๋ ์๋ก์ด ์๋๋ฅผ ์
๋ฐ์ดํธ"""
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return gr.update(value=get_random_seed())
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# Gradio ์ธํฐํ์ด์ค
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with gr.Blocks(
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theme=gr.themes.Soft(),
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max-width: 1024px;
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margin: auto;
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}
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.gallery-container {
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margin-top: 40px;
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padding: 20px;
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background: #f5f5f5;
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border-radius: 15px;
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width: 100%;
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margin: 0 auto;
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}
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.gallery-title {
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text-align: center;
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margin-bottom: 20px;
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color: #333;
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font-size: 1.5rem;
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}
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"""
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) as demo:
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gr.HTML(
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"""
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<div style="text-align: center; max-width: 800px; margin: 0 auto; padding: 20px;">
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<h1 style="font-size: 2.5rem; color: #2196F3;">3D Style Image Generator v2.0</h1>
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<p style="font-size: 1.2rem; color: #666;">Create amazing 3D-style images with AI. https://discord.gg/openfreeai </p>
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</div>
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"""
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)
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value="3d.webp"
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)
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# gallery-container ๋ถ๋ถ์ Group์ผ๋ก ๊ฐ์ธ ํ๋ฉด ์ ์ฒด์ ํ์ฅ
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with gr.Group(elem_classes="gallery-container"):
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gr.HTML("<h2 class='gallery-title'>Gallery</h2>")
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gallery_html = """
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<div style='
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display: grid;
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grid-template-columns: repeat(4, 1fr);
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gap: 20px;
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width: 100%;
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'>
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"""
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for img_file, prompt_text in SAMPLE_IMAGES.items():
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img_path = os.path.abspath(img_file)
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if os.path.exists(img_path):
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try:
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with open(img_path, "rb") as img:
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img_data = base64.b64encode(img.read()).decode()
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gallery_html += f"""
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<div style='
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border: 1px solid #ddd;
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border-radius: 10px;
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padding: 10px;
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background: white;
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box-shadow: 0 4px 8px rgba(0,0,0,0.1);
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'>
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<img src='data:image/webp;base64,{img_data}'
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style='width: 100%;
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border-radius: 8px;
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margin-bottom: 10px;'
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>
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<p style='
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margin: 5px 0;
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font-weight: bold;
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color: #333;
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padding: 10px;
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'>Prompt: {prompt_text}</p>
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</div>
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"""
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except Exception as e:
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print(f"Error loading image {img_file}: {str(e)}")
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gallery_html += "</div>"
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gr.HTML(gallery_html)
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# ์ด๋ฒคํธ ํธ๋ค๋ฌ
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generate_btn.click(
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fn=process_and_save_image,
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inputs=[height, width, steps, scales, prompt, seed],
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outputs=output
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)
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randomize_seed.click(
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fn=update_random_seed,
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inputs=None,
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outputs=seed
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)
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if __name__ == "__main__":
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demo.launch(allowed_paths=[PERSISTENT_DIR])
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