Delete app-backup.py

app-backup.py

@@ -1,338 +0,0 @@
-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)