Create app-backup.py

app-backup.py

@@ -0,0 +1,338 @@
+import random
+import gradio as gr
+import numpy as np
+import spaces
+import torch
+from diffusers import AutoencoderKL
+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"
+]
+
+# 모델 로딩: VAE 및 타일링 파이프라인 초기화
+vae = AutoencoderKL.from_pretrained(
+    "madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16
+).to("cuda")
+
+model_id = "stablediffusionapi/yamermix-v8-vae"
+pipe = StableDiffusionXLTilingPipeline.from_pretrained(
+    model_id,
+    torch_dtype=torch.float16,
+    vae=vae,
+    use_safetensors=False,  # for yammermix
+).to("cuda")
+
+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_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_parts) > 2:
+        add_kwargs["algorithm_type"] = "sde-dpmsolver++"
+    import diffusers
+    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):
+    global pipe
+    print(f"Using scheduler: {scheduler}...")
+    pipe.scheduler = select_scheduler(scheduler)
+    generator = torch.Generator("cuda").manual_seed(generation_seed)
+    
+    target_height = int(target_height)
+    target_width = int(target_width)
+    tile_height = int(tile_height)
+    tile_width = int(tile_width)
+    
+    image = pipe(
+        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]],
+        height=target_height,
+        width=target_width,
+        generator=generator,
+        num_inference_steps=steps,
+    )["images"][0]
+    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
+    best_tile_width = 0
+    best_tile_height = 0
+    best_adjusted_target_width = 0
+    best_adjusted_target_height = 0
+    found_valid_solution = False
+
+    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 = overlap_pixels * horizontal_borders
+        adjusted_target_width = tile_width * num_cols - total_horizontal_overlap
+
+        vertical_borders = num_rows - 1
+        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
+
+    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 = overlap_pixels * horizontal_borders
+            adjusted_target_width = tile_width * num_cols - total_horizontal_overlap
+
+            vertical_borders = num_rows - 1
+            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:
+                    best_tile_height = tile_height
+                    best_adjusted_target_height = adjusted_target_height
+            tile_height -= reduction_step
+
+    new_target_height = best_adjusted_target_height
+    new_target_width = best_adjusted_target_width
+    tile_width = best_tile_width
+    tile_height = best_tile_height
+
+    print("--- TILE SIZE CALCULATED VALUES ---")    
+    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
+
+def do_calc_tile(target_height, target_width, overlap_pixels, max_tile_size):    
+    new_target_height, new_target_width, tile_height, tile_width = calc_tile_size(target_height, target_width, overlap_pixels, max_tile_size)    
+    return gr.update(value=tile_height), gr.update(value=tile_width), gr.update(value=new_target_height), gr.update(value=new_target_width)
+
+def clear_result():
+    return gr.update(value=None)
+
+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
+
+# CSS 개선: 배경, 여백, 그림자 및 예제 영역 중앙 배치
+css = """
+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; }
+#examples_container {
+    margin: auto;
+    width: 90%;
+}
+#examples_row {
+    justify-content: center;
+}
+"""
+
+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 각 영역에 다른 프롬프트를 적용하여 타일링 이미지를 생성합니다.<br>
+    아래 예제를 클릭하면 입력창에 값이 채워집니다.
+</p>
+"""
+
+with gr.Blocks(css=css, title="SDXL Tiling Pipeline") as app:
+    gr.Markdown(title)
+
+    with gr.Row():
+        # 좌/중앙/우 프롬프트 및 결과 영역
+        with gr.Column(scale=7):
+            generate_button = gr.Button("Generate", elem_id="generate_btn")
+            with gr.Row():
+                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",
+                    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, scale=1)
+
+        # 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])
+
+    # 중앙에 배치된 예제 영역
+    with gr.Row(elem_id="examples_row"):
+        with gr.Column(scale=12, elem_id="examples_container"):
+            gr.Markdown("### Example Prompts")
+            gr.Examples(
+                examples=[
+                    [
+                        "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,
+                        123456789,
+                        "UniPCMultistepScheduler",
+                        1024, 1280,
+                        1024, 1920,
+                        1280
+                    ],
+                    [
+                        "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
+                    ],
+                    [
+                        "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
+                    ],
+                    [
+                        "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,
+                        150,
+                        40,
+                        1029384756,
+                        "DPMSolverMultistepScheduler-Karras-SDE",
+                        1024, 1280,
+                        1024, 1920,
+                        1280
+                    ]
+                ],
+                # 예제 클릭 시 각 입력창에 값이 채워지도록 "inputs" 인수를 추가합니다.
+                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],
+                cache_examples=False
+            )
+
+    # 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(
+        fn=randomize_seed_fn,
+        inputs=[generation_seed, randomize_seed],
+        outputs=generation_seed,
+        queue=False,
+        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],
+        outputs=result,
+    )
+
+app.launch(share=False)