Update app-backup.py

app-backup.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)}")
@@ -102,7 +98,6 @@ def save_image(image):
         print(f"Error in save_image: {str(e)}")
         return None
 
-
 # 예시 프롬프트 정의
 examples = [
     ["A 3D Star Wars Darth Vader helmet, highly detailed metallic finish"],
@@ -117,7 +112,61 @@ examples = [
     ["A 3D floating magical crystal orb with internal energy swirls"]
 ]
 
-# Gradio 인터페이스 수정
+@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()
+
+    
+def process_example(prompt):
+    return process_and_save_image(
+        height=1024,
+        width=1024,
+        steps=8,
+        scales=3.5,
+        prompt=prompt,
+        seed=get_random_seed()
+    )
+
+    
+# Gradio 인터페이스
 with gr.Blocks(
     theme=gr.themes.Soft(),
     css="""
@@ -160,57 +209,6 @@ with gr.Blocks(
                 lines=3
             )
             
-            # ... (기존 Advanced Settings 코드)
-
-        with gr.Column(scale=4, elem_classes=["fixed-width"]):
-            # 기본 이미지 설정
-            output = gr.Image(
-                label="Generated Image",
-                elem_id="output-image",
-                elem_classes=["output-image", "fixed-width"],
-                value="3d.webp"  # 기본 이미지 설정
-            )
-
-    # Examples 섹션 추가
-    gr.Examples(
-        examples=examples,
-        inputs=prompt,
-        outputs=output,
-        fn=process_and_save_image,
-        cache_examples=True,  # 예시 결과 캐싱 활성화
-        examples_per_page=5
-    )
-
-
-
-if __name__ == "__main__":
-    demo.launch(
-        allowed_paths=[PERSISTENT_DIR],
-        share=True
-    )
-
-
-
-
-
-
-
-
-
-
-
-
-    
-# Create Gradio interface
-with gr.Blocks(theme=gr.themes.Soft()) as demo:
-    with gr.Row():
-        with gr.Column(scale=3):
-            prompt = gr.Textbox(
-                label="Image Description",
-                placeholder="Describe the image you want to create...",
-                lines=3
-            )
-            
             with gr.Accordion("Advanced Settings", open=False):
                 with gr.Row():
                     height = gr.Slider(
@@ -244,9 +242,6 @@ 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(),
@@ -264,51 +259,24 @@ 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_example,  # 수정된 함수 사용
+        cache_examples=False,
+        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],