Update app.py

app.py

@@ -8,59 +8,62 @@ from mixture_tiling_sdxl import StableDiffusionXLTilingPipeline
 
 MAX_SEED = np.iinfo(np.int32).max
 SCHEDULERS = [
-                "LMSDiscreteScheduler",
-                "DEISMultistepScheduler",
-                "HeunDiscreteScheduler",
-                "EulerAncestralDiscreteScheduler",
-                "EulerDiscreteScheduler",
-                "DPMSolverMultistepScheduler",
-                "DPMSolverMultistepScheduler-Karras",
-                "DPMSolverMultistepScheduler-Karras-SDE",
-                "UniPCMultistepScheduler"
+    "LMSDiscreteScheduler",
+    "DEISMultistepScheduler",
+    "HeunDiscreteScheduler",
+    "EulerAncestralDiscreteScheduler",
+    "EulerDiscreteScheduler",
+    "DPMSolverMultistepScheduler",
+    "DPMSolverMultistepScheduler-Karras",
+    "DPMSolverMultistepScheduler-Karras-SDE",
+    "UniPCMultistepScheduler"
 ]
 
+# 로딩: VAE 및 타일링 파이프라인 모델 초기화
 vae = AutoencoderKL.from_pretrained(
     "madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16
 ).to("cuda")
 
-model_id="stablediffusionapi/yamermix-v8-vae"
+model_id = "stablediffusionapi/yamermix-v8-vae"
 pipe = StableDiffusionXLTilingPipeline.from_pretrained(
     model_id,
     torch_dtype=torch.float16,
     vae=vae,
-    use_safetensors=False, #for yammermix   
-    #variant="fp16",
+    use_safetensors=False,  # for yammermix
 ).to("cuda")
 
-pipe.enable_model_cpu_offload() #<< Enable this if you have limited VRAM
+pipe.enable_model_cpu_offload()  # VRAM이 제한된 경우 사용
 pipe.enable_vae_tiling()
 pipe.enable_vae_slicing()
 
 #region functions
 def select_scheduler(scheduler_name):
-    scheduler = scheduler_name.split("-")
-    scheduler_class_name = scheduler[0]
-    add_kwargs = {"beta_start": 0.00085, "beta_end": 0.012, "beta_schedule": "scaled_linear", "num_train_timesteps": 1000}
-    if len(scheduler) > 1:
+    scheduler_parts = scheduler_name.split("-")
+    scheduler_class_name = scheduler_parts[0]
+    add_kwargs = {
+        "beta_start": 0.00085,
+        "beta_end": 0.012,
+        "beta_schedule": "scaled_linear",
+        "num_train_timesteps": 1000
+    }
+    if len(scheduler_parts) > 1:
         add_kwargs["use_karras_sigmas"] = True
-    if len(scheduler) > 2:
+    if len(scheduler_parts) > 2:
         add_kwargs["algorithm_type"] = "sde-dpmsolver++"
     import diffusers
-    scheduler = getattr(diffusers, scheduler_class_name)    
-    scheduler = scheduler.from_config(pipe.scheduler.config, **add_kwargs) 
+    scheduler_cls = getattr(diffusers, scheduler_class_name)
+    scheduler = scheduler_cls.from_config(pipe.scheduler.config, **add_kwargs)
     return scheduler
 
-
-
 @spaces.GPU
-def predict(left_prompt, center_prompt, right_prompt, negative_prompt, left_gs, center_gs, right_gs, overlap_pixels, steps, generation_seed, scheduler, tile_height, tile_width, target_height, target_width):
+def predict(left_prompt, center_prompt, right_prompt, negative_prompt, left_gs, center_gs, right_gs,
+            overlap_pixels, steps, generation_seed, scheduler, tile_height, tile_width, target_height, target_width):
     global pipe
-    
-    # Set selected scheduler
+    # 스케줄러 선택
     print(f"Using scheduler: {scheduler}...")
     pipe.scheduler = select_scheduler(scheduler)
-
-    # Set seed
+    
+    # 시드 설정
     generator = torch.Generator("cuda").manual_seed(generation_seed)
     
     target_height = int(target_height)
@@ -68,23 +71,17 @@ def predict(left_prompt, center_prompt, right_prompt, negative_prompt, left_gs,
     tile_height = int(tile_height)
     tile_width = int(tile_width)
     
-    # Mixture of Diffusers generation
+    # 타일링된 이미지 생성 (좌/중앙/우 영역)
     image = pipe(
-        prompt=[
-            [
-                left_prompt,
-                center_prompt,
-                right_prompt,            
-            ]
-        ],
+        prompt=[[left_prompt, center_prompt, right_prompt]],
         negative_prompt=negative_prompt,
         tile_height=tile_height,
         tile_width=tile_width,
         tile_row_overlap=0,
-        tile_col_overlap=overlap_pixels,        
-        guidance_scale_tiles=[[left_gs, center_gs, right_gs]],     
+        tile_col_overlap=overlap_pixels,
+        guidance_scale_tiles=[[left_gs, center_gs, right_gs]],
         height=target_height,
-        width=target_width,               
+        width=target_width,
         generator=generator,
         num_inference_steps=steps,
     )["images"][0]
@@ -92,57 +89,53 @@ def predict(left_prompt, center_prompt, right_prompt, negative_prompt, left_gs,
     return image
 
 def calc_tile_size(target_height, target_width, overlap_pixels, max_tile_width_size=1280):
-    num_cols=3
-    num_rows=1    
-    min_tile_dimension=8
-    reduction_step=8
-    max_tile_height_size=1024
+    num_cols = 3
+    num_rows = 1    
+    min_tile_dimension = 8
+    reduction_step = 8
+    max_tile_height_size = 1024
     best_tile_width = 0
     best_tile_height = 0
     best_adjusted_target_width = 0
     best_adjusted_target_height = 0
     found_valid_solution = False
 
-    # Adjust Tile Width
+    # 타일 너비 결정
     tile_width = max_tile_width_size
     tile_height = max_tile_height_size
-
     while tile_width >= min_tile_dimension:
         horizontal_borders = num_cols - 1
-        total_horizontal_overlap_pixels = (overlap_pixels * horizontal_borders)
-        adjusted_target_width = tile_width * num_cols - total_horizontal_overlap_pixels
+        total_horizontal_overlap = overlap_pixels * horizontal_borders
+        adjusted_target_width = tile_width * num_cols - total_horizontal_overlap
 
         vertical_borders = num_rows - 1
-        total_vertical_overlap_pixels = (overlap_pixels * vertical_borders)
-        adjusted_target_height = tile_height * num_rows - total_vertical_overlap_pixels
+        total_vertical_overlap = overlap_pixels * vertical_borders
+        adjusted_target_height = tile_height * num_rows - total_vertical_overlap
 
         if tile_width <= max_tile_width_size and adjusted_target_width <= target_width:
             if adjusted_target_width > best_adjusted_target_width:
                 best_tile_width = tile_width
                 best_adjusted_target_width = adjusted_target_width
                 found_valid_solution = True
-
         tile_width -= reduction_step
 
-    # Adjust Tile Height
+    # 타일 높이 결정
     if found_valid_solution:
         tile_width = best_tile_width
         tile_height = max_tile_height_size
-
         while tile_height >= min_tile_dimension:
             horizontal_borders = num_cols - 1
-            total_horizontal_overlap_pixels = (overlap_pixels * horizontal_borders)
-            adjusted_target_width = tile_width * num_cols - total_horizontal_overlap_pixels
+            total_horizontal_overlap = overlap_pixels * horizontal_borders
+            adjusted_target_width = tile_width * num_cols - total_horizontal_overlap
 
             vertical_borders = num_rows - 1
-            total_vertical_overlap_pixels = (overlap_pixels * vertical_borders)
-            adjusted_target_height = tile_height * num_rows - total_vertical_overlap_pixels
+            total_vertical_overlap = overlap_pixels * vertical_borders
+            adjusted_target_height = tile_height * num_rows - total_vertical_overlap
         
             if tile_height <= max_tile_height_size and adjusted_target_height <= target_height:
-                 if adjusted_target_height > best_adjusted_target_height:
+                if adjusted_target_height > best_adjusted_target_height:
                     best_tile_height = tile_height
                     best_adjusted_target_height = adjusted_target_height
-
             tile_height -= reduction_step
 
     new_target_height = best_adjusted_target_height
@@ -151,15 +144,12 @@ def calc_tile_size(target_height, target_width, overlap_pixels, max_tile_width_s
     tile_height = best_tile_height
 
     print("--- TILE SIZE CALCULATED VALUES ---")    
-    print(f"Overlap pixels (requested): {overlap_pixels}")
-    print(f"Tile Height (divisible by 8, max {max_tile_height_size}): {tile_height}")
-    print(f"Tile Width (divisible by 8, max {max_tile_width_size}): {tile_width}")
-    print(f"Number of Columns (horizontal tiles): {num_cols}")
-    print(f"Number of Rows (vertical tiles): {num_rows}")
-    print(f"Original Target Height: {target_height}")
-    print(f"Original Target Width: {target_width}")
-    print(f"New Target Height (total covered height): {new_target_height}")
-    print(f"New Target Width (total covered width): {new_target_width}\n")
+    print(f"Requested Overlap Pixels: {overlap_pixels}")
+    print(f"Tile Height (max {max_tile_height_size}, divisible by 8): {tile_height}")
+    print(f"Tile Width (max {max_tile_width_size}, divisible by 8): {tile_width}")
+    print(f"Columns: {num_cols} | Rows: {num_rows}")
+    print(f"Original Target: {target_height} x {target_width}")
+    print(f"Adjusted Target: {new_target_height} x {new_target_width}\n")
 
     return new_target_height, new_target_width, tile_height, tile_width
 
@@ -170,207 +160,178 @@ def do_calc_tile(target_height, target_width, overlap_pixels, max_tile_size):
 def clear_result():
     return gr.update(value=None)
 
-def run_for_examples(left_prompt, center_prompt, right_prompt, negative_prompt, left_gs, center_gs, right_gs, overlap_pixels, steps, generation_seed, scheduler, tile_height, tile_width, target_height, target_width, max_tile_width):
-    return predict(left_prompt, center_prompt, right_prompt, negative_prompt, left_gs, center_gs, right_gs, overlap_pixels, steps, generation_seed, scheduler, tile_height, tile_width, target_height, target_width)
+def run_for_examples(left_prompt, center_prompt, right_prompt, negative_prompt, left_gs, center_gs, right_gs,
+                     overlap_pixels, steps, generation_seed, scheduler, tile_height, tile_width, target_height, target_width, max_tile_width):
+    return predict(left_prompt, center_prompt, right_prompt, negative_prompt, left_gs, center_gs, right_gs,
+                   overlap_pixels, steps, generation_seed, scheduler, tile_height, tile_width, target_height, target_width)
 
 def randomize_seed_fn(generation_seed: int, randomize_seed: bool) -> int:
     if randomize_seed:
         generation_seed = random.randint(0, MAX_SEED)
     return generation_seed
+#endregion
 
+# 개선된 CSS: 배경색, 여백, 그림자 등을 추가하여 UI를 깔끔하게 표현
 css = """
-.gradio-container .fillable { 
-    width: 95% !important;
-    max-width: unset !important;
+body { background-color: #f0f2f5; }
+.gradio-container {
+    background: #ffffff;
+    border-radius: 15px;
+    padding: 20px;
+    box-shadow: 0 4px 10px rgba(0,0,0,0.1);
 }
+.gradio-container h1 { color: #333333; }
+.fillable { width: 95% !important; max-width: unset !important; }
+"""
+
+# 제목 및 간단한 설명
+title = """
+<h1 align="center" style="margin-bottom: 0.2em;">Mixture-of-Diffusers for SDXL Tiling Pipeline 🤗</h1>
+<p align="center" style="font-size:1.1em; color:#555;">
+    좌/중앙/우 세 영역 각각에 다른 프롬프트를 적용하여 타일링 이미지를 생성합니다.<br>
+    아래의 예제나 직접 프롬프트를 입력하여 창의적인 이미지를 만들어보세요.
+</p>
 """
-title = """<h1 align="center">Mixture-of-Diffusers for SDXL Tiling Pipeline🤗</h1>           
-           <div style="display: flex; flex-direction: column; justify-content: center; align-items: center; text-align: center; overflow:hidden;">
-                <span>This <a href="https://github.com/DEVAIEXP/mixture-of-diffusers-sdxl-tiling">project</a> implements a SDXL tiling pipeline based on the original project: <a href='https://github.com/albarji/mixture-of-diffusers'>Mixture-of-Diffusers</a>. For more information, see the:                
-                <a href="https://arxiv.org/pdf/2408.06072">📜 paper </a>
-           </div>           
-           """
 
+with gr.Blocks(css=css, title="SDXL Tiling Pipeline") as app:
+    gr.Markdown(title)
 
-with gr.Blocks(css=css) as app:
-    gr.Markdown(title)    
     with gr.Row():
+        # 좌/중앙/우 영역 프롬프트 및 결과 출력 영역
         with gr.Column(scale=7):
-            generate_button = gr.Button("Generate")
+            generate_button = gr.Button("Generate", elem_id="generate_btn")
             with gr.Row():
-                with gr.Column(scale=1):
-                    gr.Markdown("### Left region")
-                    left_prompt = gr.Textbox(lines=4, 
-                                            label="Prompt for left side of the image")
-                    left_gs = gr.Slider(minimum=0, 
-                                        maximum=15, 
-                                        value=7, 
-                                        step=1, 
-                                        label="Left CFG scale")
-                with gr.Column(scale=1):
-                    gr.Markdown("### Center region")
-                    center_prompt = gr.Textbox(lines=4, 
-                                            label="Prompt for the center of the image")
-                    center_gs = gr.Slider(minimum=0, 
-                                        maximum=15, 
-                                        value=7, 
-                                        step=1, 
-                                        label="Center CFG scale")
-                with gr.Column(scale=1):
-                    gr.Markdown("### Right region")
-                    right_prompt = gr.Textbox(lines=4, 
-                                            label="Prompt for the right side of the image")
-                    right_gs = gr.Slider(minimum=0, 
-                                        maximum=15, 
-                                        value=7, 
-                                        step=1, 
-                                        label="Right CFG scale")
+                with gr.Column(variant="panel"):
+                    gr.Markdown("### Left Region")
+                    left_prompt = gr.Textbox(lines=4, placeholder="예: 울창한 숲과 햇살이 비추는 나무...", label="Left Prompt")
+                    left_gs = gr.Slider(minimum=0, maximum=15, value=7, step=1, label="Left CFG Scale")
+                with gr.Column(variant="panel"):
+                    gr.Markdown("### Center Region")
+                    center_prompt = gr.Textbox(lines=4, placeholder="예: 잔잔한 호수와 반짝이는 수면...", label="Center Prompt")
+                    center_gs = gr.Slider(minimum=0, maximum=15, value=7, step=1, label="Center CFG Scale")
+                with gr.Column(variant="panel"):
+                    gr.Markdown("### Right Region")
+                    right_prompt = gr.Textbox(lines=4, placeholder="예: 웅장한 산맥과 하늘을 가르는 구름...", label="Right Prompt")
+                    right_gs = gr.Slider(minimum=0, maximum=15, value=7, step=1, label="Right CFG Scale")
             with gr.Row():
-                negative_prompt = gr.Textbox(lines=2, 
-                                            label="Negative prompt for the image",
-                                            value="nsfw, lowres, bad anatomy, bad hands, duplicate, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, blurry")
+                negative_prompt = gr.Textbox(
+                    lines=2,
+                    label="Negative Prompt",
+                    placeholder="예: blurry, low resolution, artifacts, poor details",
+                    value="blurry, low resolution, artifacts, poor details"
+                )
             with gr.Row():
-                result = gr.Image(
-                    label="Generated Image",
-                    show_label=True, 
-                    format="png",
-                    interactive=False,
-                    # allow_preview=True,
-                    # preview=True,
-                    scale=1,
+                result = gr.Image(label="Generated Image", show_label=True, format="png", interactive=False, scale=1)
 
-                )
+        # 우측 사이드바: 파라미터 및 타일 크기 계산
+        with gr.Sidebar(label="Parameters", open=True):
+            gr.Markdown("### Generation Parameters")
+            with gr.Row():
+                height = gr.Slider(label="Target Height", value=1024, step=8, minimum=512, maximum=1024)
+                width = gr.Slider(label="Target Width", value=1280, step=8, minimum=512, maximum=3840)
+                overlap = gr.Slider(minimum=0, maximum=512, value=128, step=8, label="Tile Overlap")
+                max_tile_size = gr.Dropdown(label="Max Tile Size", choices=[1024, 1280], value=1280)
+                calc_tile = gr.Button("Calculate Tile Size")
+            with gr.Row():
+                tile_height = gr.Textbox(label="Tile Height", value=1024, interactive=False)
+                tile_width = gr.Textbox(label="Tile Width", value=1024, interactive=False)
+            with gr.Row():
+                new_target_height = gr.Textbox(label="New Image Height", value=1024, interactive=False)
+                new_target_width = gr.Textbox(label="New Image Width", value=1280, interactive=False)
+            with gr.Row():
+                steps = gr.Slider(minimum=1, maximum=50, value=30, step=1, label="Inference Steps")
+                generation_seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
+                randomize_seed = gr.Checkbox(label="Randomize Seed", value=False)
+            with gr.Row():
+                scheduler = gr.Dropdown(label="Scheduler", choices=SCHEDULERS, value=SCHEDULERS[0])
 
-    with gr.Sidebar(label="Parameters", open=True):
-        gr.Markdown("### General parameters")
-        with gr.Row():
-            height = gr.Slider(label="Height", 
-                            value=1024,
-                            step=8, 
-                            visible=True,
-                            minimum=512,
-                            maximum=1024)            
-            width = gr.Slider(label="Width",
-                            value=1280,
-                            step=8, 
-                            visible=True,
-                            minimum=512,
-                            maximum=3840)
-            overlap = gr.Slider(minimum=0, 
-                                maximum=512, 
-                                value=128, 
-                                step=8, 
-                                label="Tile Overlap")
-            max_tile_size = gr.Dropdown(label="Max. Tile Size", choices=[1024, 1280], value=1280)
-            calc_tile = gr.Button("Calculate Tile Size") 
-        with gr.Row():                       
-            tile_height = gr.Textbox(label="Tile height", value=1024, interactive=False)            
-            tile_width = gr.Textbox(label="Tile width", value=1024, interactive=False)
-        with gr.Row():
-            new_target_height = gr.Textbox(label="New image height", value=1024, interactive=False)
-            new_target_width = gr.Textbox(label="New image width", value=1024, interactive=False)
-        with gr.Row():
-            steps = gr.Slider(minimum=1,
-                            maximum=50, 
-                            value=30, 
-                            step=1, 
-                            label="Inference steps")
-            
-            generation_seed = gr.Slider(label="Seed",
-                                        minimum=0,
-                                        maximum=MAX_SEED,
-                                        step=1,
-                                        value=0)
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=False)        
-        with gr.Row():
-            scheduler = gr.Dropdown(
-                label="Schedulers",
-                choices=SCHEDULERS,
-                value=SCHEDULERS[0],
-            )
+    # 예제 탭: 목적에 맞는 예시 프롬프트 제공
     with gr.Row():
+        gr.Markdown("### Example Prompts")
         gr.Examples(
             examples=[
+                # Example 1: Serene Nature
                 [
-                    "Iron Man, repulsor rays blasting enemies in destroyed cityscape, sparks, energy trails, crumbling skyscrapers, smoke, debris, cinematic lighting, photorealistic, intense action. Focus: Iron Man.",
-                    "Captain America charging forward, vibranium shield deflecting energy blasts in destroyed cityscape, collapsing buildings, rubble streets, battle-damaged suit, determined expression, distant explosions, cinematic composition, realistic rendering. Focus: Captain America.",
-                    "Thor wielding Stormbreaker in destroyed cityscape, lightning crackling, powerful strike downwards, shattered buildings, burning debris, ground trembling, Asgardian armor, cinematic photography, realistic details. Focus: Thor.",
-                    negative_prompt.value,
-                    5, 5, 5,
-                    160,
+                    "Lush green forest with sun rays filtering through the canopy",
+                    "Crystal clear lake reflecting a vibrant sky",
+                    "Majestic mountains with snowy peaks in the distance",
+                    "blurry, low resolution, artifacts, poor details",
+                    7, 7, 7,
+                    128,
                     30,
-                    1328797844,
+                    123456789,
                     "UniPCMultistepScheduler",
-                    1024,
-                    1280,
-                    1024,                       
-                    3840,
-                    1024
+                    1024, 1280,
+                    1024, 1920,
+                    1280
                 ],
+                # Example 2: Futuristic Cityscape
                 [
-                    "A charming house in the countryside, by jakub rozalski, sunset lighting, elegant, highly detailed, smooth, sharp focus, artstation, stunning masterpiece",
-                    "A dirt road in the countryside crossing pastures, by jakub rozalski, sunset lighting, elegant, highly detailed, smooth, sharp focus, artstation, stunning masterpiece",
-                    "An old and rusty giant robot lying on a dirt road, by jakub rozalski, dark sunset lighting, elegant, highly detailed, smooth, sharp focus, artstation, stunning masterpiece",
-                    negative_prompt.value,
-                    7, 7, 7,
-                    256,
-                    30,
-                    297984183,
-                    "DPMSolverMultistepScheduler-Karras-SDE",
-                    1024,
-                    1280,
-                    1024,                       
-                    3840,
+                    "Vibrant city street with neon signs and bustling crowds",
+                    "Sleek modern skyscrapers with digital billboards",
+                    "High-speed maglev train gliding over a futuristic urban landscape",
+                    "blurry, poorly rendered, low quality, disfigured",
+                    8, 8, 8,
+                    100,
+                    35,
+                    987654321,
+                    "EulerDiscreteScheduler",
+                    1024, 1280,
+                    1024, 1920,
                     1280
                 ],
+                # Example 3: Abstract Art
                 [
-                    "Abstract decorative illustration, by joan miro and gustav klimt and marlina vera and loish, elegant, intricate, highly detailed, smooth, sharp focus, vibrant colors, artstation, stunning masterpiece",
-                    "Abstract decorative illustration, by joan miro and gustav klimt and marlina vera and loish, elegant, intricate, highly detailed, smooth, sharp focus, vibrant colors, artstation, stunning masterpiece",
-                    "Abstract decorative illustration, by joan miro and gustav klimt and marlina vera and loish, elegant, intricate, highly detailed, smooth, sharp focus, vibrant colors, artstation, stunning masterpiece",
-                    negative_prompt.value,
-                    7, 7, 7,
-                    128,
-                    30,
-                    580541206,
-                    "LMSDiscreteScheduler",
-                    1024,
-                    768,
-                    1024,     
-                    2048,
+                    "Vibrant abstract strokes with fluid, swirling patterns in cool tones",
+                    "Interlocking geometric shapes bursting with color and texture",
+                    "Dynamic composition of splattered ink with smooth gradients",
+                    "text, watermark, signature, distorted",
+                    6, 6, 6,
+                    80,
+                    25,
+                    192837465,
+                    "DPMSolverMultistepScheduler-Karras",
+                    1024, 1280,
+                    1024, 1920,
                     1280
                 ],
+                # Example 4: Fantasy Landscape
                 [
-                    "Magical diagrams and runes written with chalk on a blackboard, elegant, intricate, highly detailed, smooth, sharp focus, artstation, stunning masterpiece",
-                    "Magical diagrams and runes written with chalk on a blackboard, elegant, intricate, highly detailed, smooth, sharp focus, artstation, stunning masterpiece",
-                    "Magical diagrams and runes written with chalk on a blackboard, elegant, intricate, highly detailed, smooth, sharp focus, artstation, stunning masterpiece",
-                    negative_prompt.value,
+                    "Enchanted forest with glowing bioluminescent plants and mystical fog",
+                    "Ancient castle with towering spires bathed in moonlight",
+                    "Majestic dragon soaring above a starry night sky",
+                    "low quality, artifact, deformed, sketchy",
                     9, 9, 9,
-                    128,
-                    30,
-                    12591765619,
-                    "LMSDiscreteScheduler",
-                    1024,
-                    768,
-                    1024,                                        
-                    2048,
+                    150,
+                    40,
+                    1029384756,
+                    "DPMSolverMultistepScheduler-Karras-SDE",
+                    1024, 1280,
+                    1024, 1920,
                     1280
                 ]
             ],
-            inputs=[left_prompt, center_prompt, right_prompt, negative_prompt, left_gs, center_gs, right_gs, overlap, steps, generation_seed, scheduler, tile_height, tile_width, height, width, max_tile_size],
+            inputs=[left_prompt, center_prompt, right_prompt, negative_prompt, 
+                    left_gs, center_gs, right_gs, overlap, steps, generation_seed, 
+                    scheduler, tile_height, tile_width, height, width, max_tile_size],
             fn=run_for_examples,
             outputs=result,
             cache_examples=True
         )
-       
-    event_calc_tile_size={"fn": do_calc_tile, "inputs":[height, width, overlap, max_tile_size], "outputs":[tile_height, tile_width, new_target_height, new_target_width]}
+
+    # 이벤트 연결: 타일 사이즈 계산 및 이미지 생성
+    event_calc_tile_size = {
+        "fn": do_calc_tile,
+        "inputs": [height, width, overlap, max_tile_size],
+        "outputs": [tile_height, tile_width, new_target_height, new_target_width]
+    }
     calc_tile.click(**event_calc_tile_size)
     
     generate_button.click(
         fn=clear_result,
         inputs=None,
         outputs=result,
-    ).then(**event_calc_tile_size
-    ).then(                        
+    ).then(**event_calc_tile_size).then(
         fn=randomize_seed_fn,
         inputs=[generation_seed, randomize_seed],
         outputs=generation_seed,
@@ -378,7 +339,9 @@ with gr.Blocks(css=css) as app:
         api_name=False,
     ).then(
         fn=predict,
-        inputs=[left_prompt, center_prompt, right_prompt, negative_prompt, left_gs, center_gs, right_gs, overlap, steps, generation_seed, scheduler, tile_height, tile_width, new_target_height, new_target_width],
+        inputs=[left_prompt, center_prompt, right_prompt, negative_prompt, 
+                left_gs, center_gs, right_gs, overlap, steps, generation_seed, 
+                scheduler, tile_height, tile_width, new_target_height, new_target_width],
         outputs=result,
     )