Delete app.py

app.py

@@ -1,498 +0,0 @@
-import argparse
-import os
-os.environ['CUDA_HOME'] = '/usr/local/cuda'
-os.environ['PATH'] = os.environ['PATH'] + ':/usr/local/cuda/bin'
-from datetime import datetime
-
-import gradio as gr
-import spaces
-import numpy as np
-import torch
-from diffusers.image_processor import VaeImageProcessor
-from huggingface_hub import snapshot_download
-from PIL import Image
-torch.jit.script = lambda f: f
-from model.cloth_masker import AutoMasker, vis_mask
-from model.pipeline import CatVTONPipeline
-from utils import init_weight_dtype, resize_and_crop, resize_and_padding
-
-def parse_args():
-    parser = argparse.ArgumentParser(description="Simple example of a training script.")
-    parser.add_argument(
-        "--base_model_path",
-        type=str,
-        default="booksforcharlie/stable-diffusion-inpainting",
-        help=(
-            "The path to the base model to use for evaluation. This can be a local path or a model identifier from the Model Hub."
-        ),
-    )
-    parser.add_argument(
-        "--resume_path",
-        type=str,
-        default="zhengchong/CatVTON",
-        help=(
-            "The Path to the checkpoint of trained tryon model."
-        ),
-    )
-    parser.add_argument(
-        "--output_dir",
-        type=str,
-        default="resource/demo/output",
-        help="The output directory where the model predictions will be written.",
-    )
-    parser.add_argument(
-        "--width",
-        type=int,
-        default=768,
-        help=(
-            "The resolution for input images, all the images in the train/validation dataset will be resized to this"
-            " resolution"
-        ),
-    )
-    parser.add_argument(
-        "--height",
-        type=int,
-        default=1024,
-        help=(
-            "The resolution for input images, all the images in the train/validation dataset will be resized to this"
-            " resolution"
-        ),
-    )
-    parser.add_argument(
-        "--repaint",
-        action="store_true",
-        help="Whether to repaint the result image with the original background."
-    )
-    parser.add_argument(
-        "--allow_tf32",
-        action="store_true",
-        default=True,
-        help=(
-            "Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
-            " https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
-        ),
-    )
-    parser.add_argument(
-        "--mixed_precision",
-        type=str,
-        default="bf16",
-        choices=["no", "fp16", "bf16"],
-        help=(
-            "Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
-            " 1.10 and an Nvidia Ampere GPU.  Default to the value of accelerate config of the current system or the"
-            " flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
-        ),
-    )
-
-    args = parser.parse_args()
-    env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
-    if env_local_rank != -1 and env_local_rank != args.local_rank:
-        args.local_rank = env_local_rank
-
-    return args
-
-def image_grid(imgs, rows, cols):
-    assert len(imgs) == rows * cols
-
-    w, h = imgs[0].size
-    grid = Image.new("RGB", size=(cols * w, rows * h))
-
-    for i, img in enumerate(imgs):
-        grid.paste(img, box=(i % cols * w, i // cols * h))
-    return grid
-
-args = parse_args()
-repo_path = snapshot_download(repo_id=args.resume_path)
-
-# Pipeline
-pipeline = CatVTONPipeline(
-    base_ckpt=args.base_model_path,
-    attn_ckpt=repo_path,
-    attn_ckpt_version="mix",
-    weight_dtype=init_weight_dtype(args.mixed_precision),
-    use_tf32=args.allow_tf32,
-    device='cuda'
-)
-
-# AutoMasker
-mask_processor = VaeImageProcessor(
-    vae_scale_factor=8,
-    do_normalize=False,
-    do_binarize=True,
-    do_convert_grayscale=True
-)
-automasker = AutoMasker(
-    densepose_ckpt=os.path.join(repo_path, "DensePose"),
-    schp_ckpt=os.path.join(repo_path, "SCHP"),
-    device='cuda',
-)
-
-@spaces.GPU(duration=120)
-def submit_function(
-    person_image,
-    cloth_image,
-    cloth_type,
-    num_inference_steps,
-    guidance_scale,
-    seed,
-    show_type
-):
-    # person_image 객체에서 background와 layers[0]을 분리
-    person_image, mask = person_image["background"], person_image["layers"][0]
-    mask = Image.open(mask).convert("L")
-
-    # 만약 마스크가 전부 0(검정)이면 None 처리
-    if len(np.unique(np.array(mask))) == 1:
-        mask = None
-    else:
-        mask = np.array(mask)
-        mask[mask > 0] = 255
-        mask = Image.fromarray(mask)
-
-    tmp_folder = args.output_dir
-    date_str = datetime.now().strftime("%Y%m%d%H%M%S")
-    result_save_path = os.path.join(tmp_folder, date_str[:8], date_str[8:] + ".png")
-    if not os.path.exists(os.path.join(tmp_folder, date_str[:8])):
-        os.makedirs(os.path.join(tmp_folder, date_str[:8]))
-
-    generator = None
-    if seed != -1:
-        generator = torch.Generator(device='cuda').manual_seed(seed)
-
-    person_image = Image.open(person_image).convert("RGB")
-    cloth_image = Image.open(cloth_image).convert("RGB")
-    person_image = resize_and_crop(person_image, (args.width, args.height))
-    cloth_image = resize_and_padding(cloth_image, (args.width, args.height))
-
-    # If user didn't draw a mask
-    if mask is not None:
-        mask = resize_and_crop(mask, (args.width, args.height))
-    else:
-        mask = automasker(
-            person_image,
-            cloth_type
-        )['mask']
-    mask = mask_processor.blur(mask, blur_factor=9)
-
-    # Inference
-    result_image = pipeline(
-        image=person_image,
-        condition_image=cloth_image,
-        mask=mask,
-        num_inference_steps=num_inference_steps,
-        guidance_scale=guidance_scale,
-        generator=generator
-    )[0]
-
-    # Post-process & Save
-    masked_person = vis_mask(person_image, mask)
-    save_result_image = image_grid([person_image, masked_person, cloth_image, result_image], 1, 4)
-    save_result_image.save(result_save_path)
-
-    if show_type == "result only":
-        return result_image
-    else:
-        width, height = person_image.size
-        if show_type == "input & result":
-            condition_width = width // 2
-            conditions = image_grid([person_image, cloth_image], 2, 1)
-        else:
-            condition_width = width // 3
-            conditions = image_grid([person_image, masked_person, cloth_image], 3, 1)
-
-        conditions = conditions.resize((condition_width, height), Image.NEAREST)
-        new_result_image = Image.new("RGB", (width + condition_width + 5, height))
-        new_result_image.paste(conditions, (0, 0))
-        new_result_image.paste(result_image, (condition_width + 5, 0))
-        return new_result_image
-
-def person_example_fn(image_path):
-    return image_path
-
-# Custom CSS
-css = """
-footer {visibility: hidden}
-
-/* Main container styling */
-.gradio-container {
-    background: linear-gradient(135deg, #f5f7fa 0%, #c3cfe2 100%);
-    border-radius: 20px;
-    box-shadow: 0 8px 32px rgba(31, 38, 135, 0.15);
-}
-
-/* Header styling */
-h1, h2, h3 {
-    color: #2c3e50;
-    font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
-    text-shadow: 1px 1px 2px rgba(0,0,0,0.1);
-}
-
-/* Button styling */
-button.primary-button {
-    background: linear-gradient(45deg, #4CAF50, #45a049);
-    border: none;
-    border-radius: 10px;
-    color: white;
-    padding: 12px 24px;
-    font-weight: bold;
-    transition: all 0.3s ease;
-    box-shadow: 0 4px 15px rgba(76, 175, 80, 0.3);
-}
-
-button.primary-button:hover {
-    transform: translateY(-2px);
-    box-shadow: 0 6px 20px rgba(76, 175, 80, 0.4);
-}
-
-/* Image container styling */
-.image-container {
-    border-radius: 15px;
-    overflow: hidden;
-    box-shadow: 0 4px 15px rgba(0,0,0,0.1);
-    transition: transform 0.3s ease;
-}
-
-.image-container:hover {
-    transform: scale(1.02);
-}
-
-/* Radio button styling */
-.radio-group label {
-    background-color: #ffffff;
-    border-radius: 8px;
-    padding: 10px 15px;
-    margin: 5px;
-    cursor: pointer;
-    transition: all 0.3s ease;
-}
-
-.radio-group input:checked + label {
-    background-color: #4CAF50;
-    color: white;
-}
-
-/* Slider styling */
-.slider-container {
-    background: white;
-    padding: 15px;
-    border-radius: 10px;
-    box-shadow: 0 2px 10px rgba(0,0,0,0.05);
-}
-
-.slider {
-    height: 8px;
-    border-radius: 4px;
-    background: #e0e0e0;
-}
-
-.slider .thumb {
-    width: 20px;
-    height: 20px;
-    background: #4CAF50;
-    border-radius: 50%;
-    box-shadow: 0 2px 5px rgba(0,0,0,0.2);
-}
-
-/* Alert/warning text styling */
-.warning-text {
-    color: #ff5252;
-    font-weight: bold;
-    text-align: center;
-    padding: 10px;
-    background: rgba(255,82,82,0.1);
-    border-radius: 8px;
-    margin: 10px 0;
-}
-
-/* Example gallery styling */
-.example-gallery {
-    display: grid;
-    grid-template-columns: repeat(auto-fit, minmax(150px, 1fr));
-    gap: 15px;
-    padding: 15px;
-    background: white;
-    border-radius: 10px;
-    box-shadow: 0 2px 10px rgba(0,0,0,0.05);
-}
-
-.example-item {
-    border-radius: 8px;
-    overflow: hidden;
-    transition: transform 0.3s ease;
-}
-
-.example-item:hover {
-    transform: scale(1.05);
-}
-"""
-
-def app_gradio():
-    with gr.Blocks(theme=gr.themes.Soft(primary_hue="green", secondary_hue="blue"), css=css) as demo:
-        gr.Markdown(
-            """
-            # 👔 Fashion Fit 
-            Transform your look with AI-powered virtual clothing try-on!
-            """
-        )
-
-        with gr.Row():
-            with gr.Column(scale=1, min_width=350):
-                with gr.Group():
-                    gr.Markdown("### 📸 Upload Images")
-                    with gr.Row():
-                        image_path = gr.Image(
-                            type="filepath",
-                            interactive=True,
-                            visible=False,
-                        )
-                        person_image = gr.ImageEditor(
-                            interactive=True,
-                            label="Person Image",
-                            type="filepath",
-                            elem_classes="image-container"
-                        )
-
-                    with gr.Row():
-                        with gr.Column(scale=1, min_width=230):
-                            cloth_image = gr.Image(
-                                interactive=True,
-                                label="Clothing Item",
-                                type="filepath",
-                                elem_classes="image-container"
-                            )
-                        with gr.Column(scale=1, min_width=120):
- 
-                            cloth_type = gr.Radio(
-                                label="Clothing Type",
-                                choices=["upper", "lower", "overall"],
-                                value="upper",
-                                elem_classes="radio-group"
-                            )
-
-                submit = gr.Button("🚀 Generate Try-On", elem_classes="primary-button")
-
-
-                with gr.Accordion("⚙️ Advanced Settings", open=False):
-                    num_inference_steps = gr.Slider(
-                        label="Quality Level",
-                        minimum=10,
-                        maximum=100,
-                        step=5,
-                        value=50,
-                        elem_classes="slider-container"
-                    )
-                    guidance_scale = gr.Slider(
-                        label="Style Strength",
-                        minimum=0.0,
-                        maximum=7.5,
-                        step=0.5,
-                        value=2.5,
-                        elem_classes="slider-container"
-                    )
-                    seed = gr.Slider(
-                        label="Random Seed",
-                        minimum=-1,
-                        maximum=10000,
-                        step=1,
-                        value=42,
-                        elem_classes="slider-container"
-                    )
-                    show_type = gr.Radio(
-                        label="Display Mode",
-                        choices=["result only", "input & result", "input & mask & result"],
-                        value="input & mask & result",
-                        elem_classes="radio-group"
-                    )
-
-            with gr.Column(scale=2, min_width=500):
-                result_image = gr.Image(
-                    interactive=False,
-                    label="Final Result",
-                    elem_classes="image-container"
-                )
-                with gr.Row():
-                    root_path = "resource/demo/example"
-                    with gr.Column():
-                        gr.Markdown("#### 👤 Model Examples")
-                        # elem_classes 인자를 제거해야 오류가 사라집니다.
-                        men_exm = gr.Examples(
-                            examples=[
-                                os.path.join(root_path, "person", "men", file)
-                                for file in os.listdir(os.path.join(root_path, "person", "men"))
-                            ],
-                            examples_per_page=4,
-                            inputs=image_path,
-                            label="Men's Examples"
-                        )
-                        women_exm = gr.Examples(
-                            examples=[
-                                os.path.join(root_path, "person", "women", file)
-                                for file in os.listdir(os.path.join(root_path, "person", "women"))
-                            ],
-                            examples_per_page=4,
-                            inputs=image_path,
-                            label="Women's Examples"
-                        )
-                        gr.Markdown(
-                            '<div class="info-text">Model examples courtesy of <a href="https://huggingface.co/spaces/levihsu/OOTDiffusion">OOTDiffusion</a> and <a href="https://www.outfitanyone.org">OutfitAnyone</a></div>'
-                        )
-
-                    with gr.Column():
-                        gr.Markdown("#### 👕 Clothing Examples")
-                        condition_upper_exm = gr.Examples(
-                            examples=[
-                                os.path.join(root_path, "condition", "upper", file)
-                                for file in os.listdir(os.path.join(root_path, "condition", "upper"))
-                            ],
-                            examples_per_page=4,
-                            inputs=cloth_image,
-                            label="Upper Garments"
-                        )
-                        condition_overall_exm = gr.Examples(
-                            examples=[
-                                os.path.join(root_path, "condition", "overall", file)
-                                for file in os.listdir(os.path.join(root_path, "condition", "overall"))
-                            ],
-                            examples_per_page=4,
-                            inputs=cloth_image,
-                            label="Full Outfits"
-                        )
-                        condition_person_exm = gr.Examples(
-                            examples=[
-                                os.path.join(root_path, "condition", "person", file)
-                                for file in os.listdir(os.path.join(root_path, "condition", "person"))
-                            ],
-                            examples_per_page=4,
-                            inputs=cloth_image,
-                            label="Reference Styles"
-                        )
-                        gr.Markdown(
-                            '<div class="info-text">Clothing examples sourced from various online retailers</div>'
-                        )
-
-            image_path.change(
-                person_example_fn,
-                inputs=image_path,
-                outputs=person_image
-            )
-
-            submit.click(
-                submit_function,
-                [
-                    person_image,
-                    cloth_image,
-                    cloth_type,
-                    num_inference_steps,
-                    guidance_scale,
-                    seed,
-                    show_type,
-                ],
-                result_image,
-            )
-
-
-
-    demo.queue().launch(share=True, show_error=True)
-
-if __name__ == "__main__":
-    app_gradio()