Update app.py

app.py

@@ -56,7 +56,7 @@ pipe = FluxPipeline.from_pretrained(
     use_auth_token=HF_TOKEN
 )
 
-# Hyper-SD LoRA 로드 (인증 포함)
+# Hyper-SD LoRA 로드
 pipe.load_lora_weights(
     hf_hub_download(
         "ByteDance/Hyper-SD",
@@ -89,10 +89,6 @@ def save_image(image):
                 image = Image.fromarray(image)
                 image.save(filepath, "PNG", quality=100)
             
-            if not os.path.exists(filepath):
-                print(f"Warning: Failed to verify saved image at {filepath}")
-                return None
-                
             return filepath
         except Exception as e:
             print(f"Failed to save image: {str(e)}")
@@ -102,13 +98,102 @@ def save_image(image):
         print(f"Error in save_image: {str(e)}")
         return None
 
-# Create Gradio interface
-with gr.Blocks(theme=gr.themes.Soft()) as demo:
-    with gr.Row():
+# 예시 프롬프트 정의
+examples = [
+    ["A 3D Star Wars Darth Vader helmet, highly detailed metallic finish"],
+    ["A 3D Iron Man mask with glowing eyes and metallic red-gold finish"],
+    ["A detailed 3D Pokemon Pikachu figure with glossy surface"],
+    ["A 3D geometric abstract cube transforming into a sphere, metallic finish"],
+    ["A 3D steampunk mechanical heart with brass and copper details"],
+    ["A 3D crystal dragon with transparent iridescent scales"],
+    ["A 3D futuristic hovering drone with neon light accents"],
+    ["A 3D ancient Greek warrior helmet with ornate details"],
+    ["A 3D robotic butterfly with mechanical wings and metallic finish"],
+    ["A 3D floating magical crystal orb with internal energy swirls"]
+]
+
+@spaces.GPU
+def process_and_save_image(height=1024, width=1024, steps=8, scales=3.5, prompt="", seed=None):
+    global pipe
+    
+    if seed is None:
+        seed = torch.randint(0, 1000000, (1,)).item()
+    
+    # 한글 감지 및 번역
+    def contains_korean(text):
+        return any(ord('가') <= ord(c) <= ord('힣') for c in text)
+    
+    # 프롬프트 전처리
+    if contains_korean(prompt):
+        translated = translator(prompt)[0]['translation_text']
+        prompt = translated
+    
+    formatted_prompt = f"wbgmsst, 3D, {prompt} ,white background"
+    
+    with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16), timer("inference"):
+        try:
+            generated_image = pipe(
+                prompt=[formatted_prompt],
+                generator=torch.Generator().manual_seed(int(seed)),
+                num_inference_steps=int(steps),
+                guidance_scale=float(scales),
+                height=int(height),
+                width=int(width),
+                max_sequence_length=256
+            ).images[0]
+            
+            saved_path = save_image(generated_image)
+            if saved_path is None:
+                print("Warning: Failed to save generated image")
+            
+            return generated_image
+        except Exception as e:
+            print(f"Error in image generation: {str(e)}")
+            return None
+
+def get_random_seed():
+    return torch.randint(0, 1000000, (1,)).item()
+
+# Gradio 인터페이스
+with gr.Blocks(
+    theme=gr.themes.Soft(),
+    css="""
+        .container {
+            background: linear-gradient(to bottom right, #1a1a1a, #4a4a4a);
+            border-radius: 20px;
+            padding: 20px;
+        }
+        .generate-btn {
+            background: linear-gradient(45deg, #2196F3, #00BCD4);
+            border: none;
+            color: white;
+            font-weight: bold;
+            border-radius: 10px;
+        }
+        .output-image {
+            border-radius: 15px;
+            box-shadow: 0 8px 16px rgba(0,0,0,0.2);
+        }
+        .fixed-width {
+            max-width: 1024px;
+            margin: auto;
+        }
+    """
+) as demo:
+    gr.HTML(
+        """
+        <div style="text-align: center; max-width: 800px; margin: 0 auto; padding: 20px;">
+            <h1 style="font-size: 2.5rem; color: #2196F3;">3D Style Image Generator</h1>
+            <p style="font-size: 1.2rem; color: #666;">Create amazing 3D-style images with AI</p>
+        </div>
+        """
+    )
+    
+    with gr.Row(elem_classes="container"):
         with gr.Column(scale=3):
             prompt = gr.Textbox(
                 label="Image Description",
-                placeholder="Describe the image you want to create...",
+                placeholder="Describe the 3D image you want to create...",
                 lines=3
             )
             
@@ -145,9 +230,6 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
                         value=3.5
                     )
                 
-                def get_random_seed():
-                    return torch.randint(0, 1000000, (1,)).item()
-                
                 seed = gr.Number(
                     label="Seed (random by default, set for reproducibility)",
                     value=get_random_seed(),
@@ -165,51 +247,24 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
             output = gr.Image(
                 label="Generated Image",
                 elem_id="output-image",
-                elem_classes=["output-image", "fixed-width"]
+                elem_classes=["output-image", "fixed-width"],
+                value="3d.webp"
             )
 
-    @spaces.GPU
-    def process_and_save_image(height, width, steps, scales, prompt, seed):
-        global pipe
-        
-        # 한글 감지 및 번역
-        def contains_korean(text):
-            return any(ord('가') <= ord(c) <= ord('힣') for c in text)
-        
-        # 프롬프트 전처리
-        if contains_korean(prompt):
-            # 한글을 영어로 번역
-            translated = translator(prompt)[0]['translation_text']
-            prompt = translated
-        
-        # 프롬프트 형식 강제
-        formatted_prompt = f"wbgmsst, 3D, {prompt} ,white background"
-        
-        with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16), timer("inference"):
-            try:
-                generated_image = pipe(
-                    prompt=[formatted_prompt],
-                    generator=torch.Generator().manual_seed(int(seed)),
-                    num_inference_steps=int(steps),
-                    guidance_scale=float(scales),
-                    height=int(height),
-                    width=int(width),
-                    max_sequence_length=256
-                ).images[0]
-                
-                saved_path = save_image(generated_image)
-                if saved_path is None:
-                    print("Warning: Failed to save generated image")
-                
-                return generated_image
-            except Exception as e:
-                print(f"Error in image generation: {str(e)}")
-                return None
-    
+    # Examples 섹션
+    gr.Examples(
+        examples=examples,
+        inputs=prompt,
+        outputs=output,
+        fn=process_and_save_image,
+        cache_examples=True,
+        examples_per_page=5
+    )
+
     def update_seed():
         return get_random_seed()
 
-    # Click event handlers inside gr.Blocks context
+    # 이벤트 핸들러
     generate_btn.click(
         process_and_save_image,
         inputs=[height, width, steps, scales, prompt, seed],