gradio_app.py

# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT
# except for the third-party components listed below.
# Hunyuan 3D does not impose any additional limitations beyond what is outlined
# in the repsective licenses of these third-party components.
# Users must comply with all terms and conditions of original licenses of these third-party
# components and must ensure that the usage of the third party components adheres to
# all relevant laws and regulations.

# For avoidance of doubts, Hunyuan 3D means the large language models and
# their software and algorithms, including trained model weights, parameters (including
# optimizer states), machine-learning model code, inference-enabling code, training-enabling code,
# fine-tuning enabling code and other elements of the foregoing made publicly available
# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT.

# Apply torchvision compatibility fix before other imports

import sys

sys.path.insert(0, "./hy3dshape")
sys.path.insert(0, "./hy3dpaint")

pythonpath = sys.executable
print(pythonpath)

try:
    from torchvision_fix import apply_fix

    apply_fix()
except ImportError:
    print(
        "Warning: torchvision_fix module not found, proceeding without compatibility fix"
    )
except Exception as e:
    print(f"Warning: Failed to apply torchvision fix: {e}")


import os
import random
import shutil
import subprocess
import time
from glob import glob
from pathlib import Path

import gradio as gr
import torch
import trimesh
import uvicorn
from fastapi import FastAPI
from fastapi.staticfiles import StaticFiles
import uuid
import numpy as np

from hy3dshape.utils import logger
from hy3dpaint.convert_utils import create_glb_with_pbr_materials


MAX_SEED = 1e7
ENV = "Local"  # "Huggingface"
if ENV == "Huggingface":
    """
    Setup environment for running on Huggingface platform.

    This block performs the following:
    - Changes directory to the differentiable renderer folder and runs a shell 
        script to compile the mesh painter.
    - Installs a custom rasterizer wheel package via pip.

    Note:
        This setup assumes the script is running in the Huggingface environment 
        with the specified directory structure.
    """
    import os, spaces, subprocess, sys, shlex
    from spaces import zero

    def install_cuda_toolkit():
        # CUDA_TOOLKIT_URL = "https://developer.download.nvidia.com/compute/cuda/11.8.0/local_installers/cuda_11.8.0_520.61.05_linux.run"
        CUDA_TOOLKIT_URL = "https://developer.download.nvidia.com/compute/cuda/12.2.0/local_installers/cuda_12.2.0_535.54.03_linux.run"
        CUDA_TOOLKIT_FILE = "/tmp/%s" % os.path.basename(CUDA_TOOLKIT_URL)
        subprocess.call(["wget", "-q", CUDA_TOOLKIT_URL, "-O", CUDA_TOOLKIT_FILE])
        subprocess.call(["chmod", "+x", CUDA_TOOLKIT_FILE])
        subprocess.call([CUDA_TOOLKIT_FILE, "--silent", "--toolkit"])

        os.environ["CUDA_HOME"] = "/usr/local/cuda"
        os.environ["PATH"] = "%s/bin:%s" % (os.environ["CUDA_HOME"], os.environ["PATH"])
        os.environ["LD_LIBRARY_PATH"] = "%s/lib:%s" % (
            os.environ["CUDA_HOME"],
            ""
            if "LD_LIBRARY_PATH" not in os.environ
            else os.environ["LD_LIBRARY_PATH"],
        )
        # Fix: arch_list[-1] += '+PTX'; IndexError: list index out of range
        os.environ["TORCH_CUDA_ARCH_LIST"] = "8.0;8.6"

    def prepare_env():
        # print('install custom')
        # os.system(f"cd /home/user/app/hy3dpaint/custom_rasterizer && {pythonpath} -m pip install -e .")
        # os.system(f"cd /home/user/app/hy3dpaint/packages/custom_rasterizer && pip install -e .")
        subprocess.run(
            shlex.split(
                "pip install custom_rasterizer-0.1-cp310-cp310-linux_x86_64.whl"
            ),
            check=True,
        )

        print(
            "cd /home/user/app/hy3dpaint/differentiable_renderer/ && bash compile_mesh_painter.sh"
        )
        os.system(
            "cd /home/user/app/hy3dpaint/DifferentiableRenderer && bash compile_mesh_painter.sh"
        )
        # print("wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth -P /home/user/app/hy3dpaint/ckpt")
        # os.system("wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth -P /home/user/app/hy3dpaint/ckpt")

    def check():
        import custom_rasterizer

        print(type(custom_rasterizer))
        print(dir(custom_rasterizer))
        print(getattr(custom_rasterizer, "__file__", None))

        package_dir = None
        if hasattr(custom_rasterizer, "__file__") and custom_rasterizer.__file__:
            package_dir = os.path.dirname(custom_rasterizer.__file__)
        elif hasattr(custom_rasterizer, "__path__"):
            package_dir = list(custom_rasterizer.__path__)[0]
        else:
            raise RuntimeError("Cannot determine package path")
        print(package_dir)

        for root, dirs, files in os.walk(package_dir):
            level = root.replace(package_dir, "").count(os.sep)
            indent = " " * 4 * level
            print(f"{indent}{os.path.basename(root)}/")
            subindent = " " * 4 * (level + 1)
            for f in files:
                print(f"{subindent}{f}")

    # print(torch.__version__)
    # install_cuda_toolkit()
    print(torch.__version__)
    prepare_env()
    check()

else:
    """
    Define a dummy `spaces` module with a GPU decorator class for local environment.

    The GPU decorator is a no-op that simply returns the decorated function unchanged.
    This allows code that uses the `spaces.GPU` decorator to run without modification locally.
    """

    import os, spaces, subprocess, sys, shlex

    def install_cuda_toolkit():
        CUDA_TOOLKIT_URL = "https://developer.download.nvidia.com/compute/cuda/11.8.0/local_installers/cuda_11.8.0_520.61.05_linux.run"
        # CUDA_TOOLKIT_URL = "https://developer.download.nvidia.com/compute/cuda/12.2.0/local_installers/cuda_12.2.0_535.54.03_linux.run"
        CUDA_TOOLKIT_FILE = "/tmp/%s" % os.path.basename(CUDA_TOOLKIT_URL)
        subprocess.call(["wget", "-q", CUDA_TOOLKIT_URL, "-O", CUDA_TOOLKIT_FILE])
        subprocess.call(["chmod", "+x", CUDA_TOOLKIT_FILE])
        subprocess.call([CUDA_TOOLKIT_FILE, "--silent", "--toolkit"])

        os.environ["CUDA_HOME"] = "/usr/local/cuda"
        os.environ["PATH"] = "%s/bin:%s" % (os.environ["CUDA_HOME"], os.environ["PATH"])
        os.environ["LD_LIBRARY_PATH"] = "%s/lib:%s" % (
            os.environ["CUDA_HOME"],
            ""
            if "LD_LIBRARY_PATH" not in os.environ
            else os.environ["LD_LIBRARY_PATH"],
        )
        # Fix: arch_list[-1] += '+PTX'; IndexError: list index out of range
        os.environ["TORCH_CUDA_ARCH_LIST"] = "8.0;8.6"

    def prepare_env():
        # print('install custom')
        # os.system(f"cd /home/user/app/hy3dpaint/custom_rasterizer && {pythonpath} -m pip install -e .")
        # os.system(f"cd /home/user/app/hy3dpaint/packages/custom_rasterizer && pip install -e .")
        subprocess.run(
            shlex.split(
                "pip install custom_rasterizer-0.1-cp310-cp310-linux_x86_64.whl"
            ),
            check=True,
        )

        print(
            "cd /home/user/app/hy3dpaint/differentiable_renderer/ && bash compile_mesh_painter.sh"
        )
        os.system(
            "cd /home/user/app/hy3dpaint/DifferentiableRenderer && bash compile_mesh_painter.sh"
        )
        # print("wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth -P /home/user/app/hy3dpaint/ckpt")
        # os.system("wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth -P /home/user/app/hy3dpaint/ckpt")

    def check():
        import custom_rasterizer

        print(type(custom_rasterizer))
        print(dir(custom_rasterizer))
        print(getattr(custom_rasterizer, "__file__", None))

        package_dir = None
        if hasattr(custom_rasterizer, "__file__") and custom_rasterizer.__file__:
            package_dir = os.path.dirname(custom_rasterizer.__file__)
        elif hasattr(custom_rasterizer, "__path__"):
            package_dir = list(custom_rasterizer.__path__)[0]
        else:
            raise RuntimeError("Cannot determine package path")
        print(package_dir)

        for root, dirs, files in os.walk(package_dir):
            level = root.replace(package_dir, "").count(os.sep)
            indent = " " * 4 * level
            print(f"{indent}{os.path.basename(root)}/")
            subindent = " " * 4 * (level + 1)
            for f in files:
                print(f"{subindent}{f}")

    # print(torch.__version__)
    # install_cuda_toolkit()
    print(torch.__version__)
    prepare_env()
    check()

    class spaces:
        class GPU:
            def __init__(self, duration=60):
                self.duration = duration

            def __call__(self, func):
                return func


def get_example_img_list():
    """
    Load and return a sorted list of example image file paths.

    Searches recursively for PNG images under the './assets/example_images/' directory.

    Returns:
        list[str]: Sorted list of file paths to example PNG images.
    """
    print("Loading example img list ...")
    return sorted(glob("./assets/example_images/**/*.png", recursive=True))


def get_example_txt_list():
    """
    Load and return a list of example text prompts.

    Reads lines from the './assets/example_prompts.txt' file, stripping whitespace.

    Returns:
        list[str]: List of example text prompts.
    """
    print("Loading example txt list ...")
    txt_list = list()
    for line in open("./assets/example_prompts.txt", encoding="utf-8"):
        txt_list.append(line.strip())
    return txt_list


def gen_save_folder(max_size=200):
    """
    Generate a new save folder inside SAVE_DIR, maintaining a maximum number of folders.

    If the number of existing folders in SAVE_DIR exceeds `max_size`, the oldest folder is removed.

    Args:
        max_size (int, optional): Maximum number of folders to keep in SAVE_DIR. Defaults to 200.

    Returns:
        str: Path to the newly created save folder.
    """
    os.makedirs(SAVE_DIR, exist_ok=True)
    dirs = [f for f in Path(SAVE_DIR).iterdir() if f.is_dir()]
    if len(dirs) >= max_size:
        oldest_dir = min(dirs, key=lambda x: x.stat().st_ctime)
        shutil.rmtree(oldest_dir)
        print(f"Removed the oldest folder: {oldest_dir}")
    new_folder = os.path.join(SAVE_DIR, str(uuid.uuid4()))
    os.makedirs(new_folder, exist_ok=True)
    print(f"Created new folder: {new_folder}")
    return new_folder


# Removed complex PBR conversion functions - using simple trimesh-based conversion
def export_mesh(mesh, save_folder, textured=False, type="glb"):
    """
    Export a mesh to a file in the specified folder, optionally including textures.

    Args:
        mesh (trimesh.Trimesh): The mesh object to export.
        save_folder (str): Directory path where the mesh file will be saved.
        textured (bool, optional): Whether to include textures/normals in the export. Defaults to False.
        type (str, optional): File format to export ('glb' or 'obj' supported). Defaults to 'glb'.

    Returns:
        str: The full path to the exported mesh file.
    """
    if textured:
        path = os.path.join(save_folder, f"textured_mesh.{type}")
    else:
        path = os.path.join(save_folder, f"white_mesh.{type}")
    if type not in ["glb", "obj"]:
        mesh.export(path)
    else:
        mesh.export(path, include_normals=textured)
    return path


def quick_convert_with_obj2gltf(obj_path: str, glb_path: str) -> bool:
    # 执行转换
    textures = {
        "albedo": obj_path.replace(".obj", ".jpg"),
        "metallic": obj_path.replace(".obj", "_metallic.jpg"),
        "roughness": obj_path.replace(".obj", "_roughness.jpg"),
    }
    create_glb_with_pbr_materials(obj_path, textures, glb_path)


def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
    return seed


def build_model_viewer_html(save_folder, height=660, width=790, textured=False):
    # Remove first folder from path to make relative path
    if textured:
        related_path = f"./textured_mesh.glb"
        template_name = "./assets/modelviewer-textured-template.html"
        output_html_path = os.path.join(save_folder, f"textured_mesh.html")
    else:
        related_path = f"./white_mesh.glb"
        template_name = "./assets/modelviewer-template.html"
        output_html_path = os.path.join(save_folder, f"white_mesh.html")
    offset = 50 if textured else 10
    with open(os.path.join(CURRENT_DIR, template_name), "r", encoding="utf-8") as f:
        template_html = f.read()

    with open(output_html_path, "w", encoding="utf-8") as f:
        template_html = template_html.replace("#height#", f"{height - offset}")
        template_html = template_html.replace("#width#", f"{width}")
        template_html = template_html.replace("#src#", f"{related_path}/")
        f.write(template_html)

    rel_path = os.path.relpath(output_html_path, SAVE_DIR)
    iframe_tag = f'<iframe src="/static/{rel_path}" \
height="{height}" width="100%" frameborder="0"></iframe>'
    print(
        f"Find html file {output_html_path}, \
{os.path.exists(output_html_path)}, relative HTML path is /static/{rel_path}"
    )

    return f"""
        <div style='height: {height}; width: 100%;'>
        {iframe_tag}
        </div>
    """


@spaces.GPU(duration=60)
def _gen_shape(
    caption=None,
    image=None,
    mv_image_front=None,
    mv_image_back=None,
    mv_image_left=None,
    mv_image_right=None,
    steps=50,
    guidance_scale=7.5,
    seed=1234,
    octree_resolution=256,
    check_box_rembg=False,
    num_chunks=200000,
    randomize_seed: bool = False,
):
    if not MV_MODE and image is None and caption is None:
        raise gr.Error("Please provide either a caption or an image.")
    if MV_MODE:
        if (
            mv_image_front is None
            and mv_image_back is None
            and mv_image_left is None
            and mv_image_right is None
        ):
            raise gr.Error("Please provide at least one view image.")
        image = {}
        if mv_image_front:
            image["front"] = mv_image_front
        if mv_image_back:
            image["back"] = mv_image_back
        if mv_image_left:
            image["left"] = mv_image_left
        if mv_image_right:
            image["right"] = mv_image_right

    seed = int(randomize_seed_fn(seed, randomize_seed))

    octree_resolution = int(octree_resolution)
    if caption:
        print("prompt is", caption)
    save_folder = gen_save_folder()
    stats = {
        "model": {
            "shapegen": f"{args.model_path}/{args.subfolder}",
            "texgen": f"{args.texgen_model_path}",
        },
        "params": {
            "caption": caption,
            "steps": steps,
            "guidance_scale": guidance_scale,
            "seed": seed,
            "octree_resolution": octree_resolution,
            "check_box_rembg": check_box_rembg,
            "num_chunks": num_chunks,
        },
    }
    time_meta = {}

    if image is None:
        start_time = time.time()
        try:
            image = t2i_worker(caption)
        except Exception as e:
            raise gr.Error(
                f"Text to 3D is disable. \
            Please enable it by `python gradio_app.py --enable_t23d`."
            )
        time_meta["text2image"] = time.time() - start_time

    # remove disk io to make responding faster, uncomment at your will.
    # image.save(os.path.join(save_folder, 'input.png'))
    if MV_MODE:
        start_time = time.time()
        for k, v in image.items():
            if check_box_rembg or v.mode == "RGB":
                img = rmbg_worker(v.convert("RGB"))
                image[k] = img
        time_meta["remove background"] = time.time() - start_time
    else:
        if check_box_rembg or image.mode == "RGB":
            start_time = time.time()
            image = rmbg_worker(image.convert("RGB"))
            time_meta["remove background"] = time.time() - start_time

    # remove disk io to make responding faster, uncomment at your will.
    # image.save(os.path.join(save_folder, 'rembg.png'))

    # image to white model
    start_time = time.time()

    generator = torch.Generator()
    generator = generator.manual_seed(int(seed))
    outputs = i23d_worker(
        image=image,
        num_inference_steps=steps,
        guidance_scale=guidance_scale,
        generator=generator,
        octree_resolution=octree_resolution,
        num_chunks=num_chunks,
        output_type="mesh",
    )
    time_meta["shape generation"] = time.time() - start_time
    logger.info("---Shape generation takes %s seconds ---" % (time.time() - start_time))

    tmp_start = time.time()
    mesh = export_to_trimesh(outputs)[0]
    time_meta["export to trimesh"] = time.time() - tmp_start

    stats["number_of_faces"] = mesh.faces.shape[0]
    stats["number_of_vertices"] = mesh.vertices.shape[0]

    stats["time"] = time_meta
    main_image = image if not MV_MODE else image["front"]
    return mesh, main_image, save_folder, stats, seed


@spaces.GPU(duration=130)
def generation_all(
    caption=None,
    image=None,
    mv_image_front=None,
    mv_image_back=None,
    mv_image_left=None,
    mv_image_right=None,
    steps=50,
    guidance_scale=7.5,
    seed=1234,
    octree_resolution=256,
    check_box_rembg=False,
    num_chunks=200000,
    randomize_seed: bool = False,
):
    start_time_0 = time.time()
    mesh, image, save_folder, stats, seed = _gen_shape(
        caption,
        image,
        mv_image_front=mv_image_front,
        mv_image_back=mv_image_back,
        mv_image_left=mv_image_left,
        mv_image_right=mv_image_right,
        steps=steps,
        guidance_scale=guidance_scale,
        seed=seed,
        octree_resolution=octree_resolution,
        check_box_rembg=check_box_rembg,
        num_chunks=num_chunks,
        randomize_seed=randomize_seed,
    )
    path = export_mesh(mesh, save_folder, textured=False)

    print(path)
    print("=" * 40)

    # tmp_time = time.time()
    # mesh = floater_remove_worker(mesh)
    # mesh = degenerate_face_remove_worker(mesh)
    # logger.info("---Postprocessing takes %s seconds ---" % (time.time() - tmp_time))
    # stats['time']['postprocessing'] = time.time() - tmp_time

    tmp_time = time.time()
    mesh = face_reduce_worker(mesh)

    # path = export_mesh(mesh, save_folder, textured=False, type='glb')
    path = export_mesh(
        mesh, save_folder, textured=False, type="obj"
    )  # 这样操作也会 core dump

    logger.info("---Face Reduction takes %s seconds ---" % (time.time() - tmp_time))
    stats["time"]["face reduction"] = time.time() - tmp_time

    tmp_time = time.time()

    text_path = os.path.join(save_folder, f"textured_mesh.obj")
    path_textured = tex_pipeline(
        mesh_path=path, image_path=image, output_mesh_path=text_path, save_glb=False
    )

    logger.info("---Texture Generation takes %s seconds ---" % (time.time() - tmp_time))
    stats["time"]["texture generation"] = time.time() - tmp_time

    tmp_time = time.time()
    # Convert textured OBJ to GLB using obj2gltf with PBR support
    glb_path_textured = os.path.join(save_folder, "textured_mesh.glb")
    conversion_success = quick_convert_with_obj2gltf(path_textured, glb_path_textured)

    logger.info(
        "---Convert textured OBJ to GLB takes %s seconds ---" % (time.time() - tmp_time)
    )
    stats["time"]["convert textured OBJ to GLB"] = time.time() - tmp_time
    stats["time"]["total"] = time.time() - start_time_0
    model_viewer_html_textured = build_model_viewer_html(
        save_folder, height=HTML_HEIGHT, width=HTML_WIDTH, textured=True
    )
    if args.low_vram_mode:
        torch.cuda.empty_cache()
    return (
        gr.update(value=path),
        gr.update(value=glb_path_textured),
        model_viewer_html_textured,
        stats,
        seed,
    )


@spaces.GPU(duration=60)
def shape_generation(
    caption=None,
    image=None,
    mv_image_front=None,
    mv_image_back=None,
    mv_image_left=None,
    mv_image_right=None,
    steps=50,
    guidance_scale=7.5,
    seed=1234,
    octree_resolution=256,
    check_box_rembg=False,
    num_chunks=200000,
    randomize_seed: bool = False,
):
    start_time_0 = time.time()
    mesh, image, save_folder, stats, seed = _gen_shape(
        caption,
        image,
        mv_image_front=mv_image_front,
        mv_image_back=mv_image_back,
        mv_image_left=mv_image_left,
        mv_image_right=mv_image_right,
        steps=steps,
        guidance_scale=guidance_scale,
        seed=seed,
        octree_resolution=octree_resolution,
        check_box_rembg=check_box_rembg,
        num_chunks=num_chunks,
        randomize_seed=randomize_seed,
    )
    stats["time"]["total"] = time.time() - start_time_0
    mesh.metadata["extras"] = stats

    path = export_mesh(mesh, save_folder, textured=False)
    model_viewer_html = build_model_viewer_html(
        save_folder, height=HTML_HEIGHT, width=HTML_WIDTH
    )
    if args.low_vram_mode:
        torch.cuda.empty_cache()
    return (
        gr.update(value=path),
        model_viewer_html,
        stats,
        seed,
    )


def build_app():
    title = "High Resolution Textured 3D Assets Generation"
    if MV_MODE:
        title = "Image to 3D Generation with 1-4 Views"
    if "mini" in args.subfolder:
        title = "Strong 0.6B Image to Shape Generator"

    title = "Image to 3D Generation"

    if TURBO_MODE:
        title = title.replace(":", "-Turbo: Fast ")

    title_html = f"""
    <div style="font-size: 2em; font-weight: bold; text-align: center; margin-bottom: 5px">

    {title}
    </div>
    <div align="center">
    Imagine Team
    </div>
    """
    custom_css = """
    .app.svelte-wpkpf6.svelte-wpkpf6:not(.fill_width) {
        max-width: 1480px;
    }
    .mv-image button .wrap {
        font-size: 10px;
    }

    .mv-image .icon-wrap {
        width: 20px;
    }

    """

    with gr.Blocks(
        theme=gr.themes.Base(),
        title="Image to 3D Generation",
        analytics_enabled=False,
        css=custom_css,
    ) as demo:
        gr.HTML(title_html)

        with gr.Row():
            with gr.Column(scale=3):
                with gr.Tabs(selected="tab_img_prompt") as tabs_prompt:
                    with gr.Tab(
                        "Image Prompt", id="tab_img_prompt", visible=not MV_MODE
                    ) as tab_ip:
                        image = gr.Image(
                            label="Image", type="pil", image_mode="RGBA", height=290
                        )
                        caption = gr.State(None)
                    #                    with gr.Tab('Text Prompt', id='tab_txt_prompt', visible=HAS_T2I and not MV_MODE) as tab_tp:
                    #                        caption = gr.Textbox(label='Text Prompt',
                    #                                             placeholder='HunyuanDiT will be used to generate image.',
                    #                                             info='Example: A 3D model of a cute cat, white background')
                    with gr.Tab("MultiView Prompt", visible=MV_MODE) as tab_mv:
                        # gr.Label('Please upload at least one front image.')
                        with gr.Row():
                            mv_image_front = gr.Image(
                                label="Front",
                                type="pil",
                                image_mode="RGBA",
                                height=140,
                                min_width=100,
                                elem_classes="mv-image",
                            )
                            mv_image_back = gr.Image(
                                label="Back",
                                type="pil",
                                image_mode="RGBA",
                                height=140,
                                min_width=100,
                                elem_classes="mv-image",
                            )
                        with gr.Row():
                            mv_image_left = gr.Image(
                                label="Left",
                                type="pil",
                                image_mode="RGBA",
                                height=140,
                                min_width=100,
                                elem_classes="mv-image",
                            )
                            mv_image_right = gr.Image(
                                label="Right",
                                type="pil",
                                image_mode="RGBA",
                                height=140,
                                min_width=100,
                                elem_classes="mv-image",
                            )

                with gr.Row():
                    btn = gr.Button(value="Gen Shape", variant="primary", min_width=100)
                    btn_all = gr.Button(
                        value="Gen Textured Shape",
                        variant="primary",
                        visible=HAS_TEXTUREGEN,
                        min_width=100,
                    )

                with gr.Group():
                    file_out = gr.File(label="File", visible=False)
                    file_out2 = gr.File(label="File", visible=False)

                with gr.Tabs(selected="tab_options" if TURBO_MODE else "tab_export"):
                    with gr.Tab("Options", id="tab_options", visible=TURBO_MODE):
                        gen_mode = gr.Radio(
                            label="Generation Mode",
                            info="Recommendation: Turbo for most cases, \
Fast for very complex cases, Standard seldom use.",
                            choices=["Turbo", "Fast", "Standard"],
                            value="Turbo",
                        )
                        decode_mode = gr.Radio(
                            label="Decoding Mode",
                            info="The resolution for exporting mesh from generated vectset",
                            choices=["Low", "Standard", "High"],
                            value="Standard",
                        )
                    with gr.Tab("Advanced Options", id="tab_advanced_options"):
                        with gr.Row():
                            check_box_rembg = gr.Checkbox(
                                value=True, label="Remove Background", min_width=100
                            )
                            randomize_seed = gr.Checkbox(
                                label="Randomize seed", value=True, min_width=100
                            )
                        seed = gr.Slider(
                            label="Seed",
                            minimum=0,
                            maximum=MAX_SEED,
                            step=1,
                            value=1234,
                            min_width=100,
                        )
                        with gr.Row():
                            num_steps = gr.Slider(
                                maximum=100,
                                minimum=1,
                                value=5 if "turbo" in args.subfolder else 30,
                                step=1,
                                label="Inference Steps",
                            )
                            octree_resolution = gr.Slider(
                                maximum=512,
                                minimum=16,
                                value=256,
                                label="Octree Resolution",
                            )
                        with gr.Row():
                            cfg_scale = gr.Number(
                                value=5.0, label="Guidance Scale", min_width=100
                            )
                            num_chunks = gr.Slider(
                                maximum=5000000,
                                minimum=1000,
                                value=8000,
                                label="Number of Chunks",
                                min_width=100,
                            )
                    with gr.Tab("Export", id="tab_export"):
                        with gr.Row():
                            file_type = gr.Dropdown(
                                label="File Type",
                                choices=SUPPORTED_FORMATS,
                                value="glb",
                                min_width=100,
                            )
                            reduce_face = gr.Checkbox(
                                label="Simplify Mesh", value=False, min_width=100
                            )
                            export_texture = gr.Checkbox(
                                label="Include Texture",
                                value=False,
                                visible=False,
                                min_width=100,
                            )
                        target_face_num = gr.Slider(
                            maximum=1000000,
                            minimum=100,
                            value=10000,
                            label="Target Face Number",
                        )
                        with gr.Row():
                            confirm_export = gr.Button(value="Transform", min_width=100)
                            file_export = gr.DownloadButton(
                                label="Download",
                                variant="primary",
                                interactive=False,
                                min_width=100,
                            )

            with gr.Column(scale=6):
                with gr.Tabs(selected="gen_mesh_panel") as tabs_output:
                    with gr.Tab("Generated Mesh", id="gen_mesh_panel"):
                        html_gen_mesh = gr.HTML(HTML_OUTPUT_PLACEHOLDER, label="Output")
                    with gr.Tab("Exporting Mesh", id="export_mesh_panel"):
                        html_export_mesh = gr.HTML(
                            HTML_OUTPUT_PLACEHOLDER, label="Output"
                        )
                    with gr.Tab("Mesh Statistic", id="stats_panel"):
                        stats = gr.Json({}, label="Mesh Stats")

            with gr.Column(scale=3 if MV_MODE else 2):
                with gr.Tabs(selected="tab_img_gallery") as gallery:
                    with gr.Tab(
                        "Image to 3D Gallery", id="tab_img_gallery", visible=not MV_MODE
                    ) as tab_gi:
                        with gr.Row():
                            gr.Examples(
                                examples=example_is,
                                inputs=[image],
                                label=None,
                                examples_per_page=18,
                            )

        tab_ip.select(fn=lambda: gr.update(selected="tab_img_gallery"), outputs=gallery)
        # if HAS_T2I:
        #    tab_tp.select(fn=lambda: gr.update(selected='tab_txt_gallery'), outputs=gallery)

        btn.click(
            shape_generation,
            inputs=[
                caption,
                image,
                mv_image_front,
                mv_image_back,
                mv_image_left,
                mv_image_right,
                num_steps,
                cfg_scale,
                seed,
                octree_resolution,
                check_box_rembg,
                num_chunks,
                randomize_seed,
            ],
            outputs=[file_out, html_gen_mesh, stats, seed],
        ).then(
            lambda: (
                gr.update(visible=False, value=False),
                gr.update(interactive=True),
                gr.update(interactive=True),
                gr.update(interactive=False),
            ),
            outputs=[export_texture, reduce_face, confirm_export, file_export],
        ).then(
            lambda: gr.update(selected="gen_mesh_panel"),
            outputs=[tabs_output],
        )

        btn_all.click(
            generation_all,
            inputs=[
                caption,
                image,
                mv_image_front,
                mv_image_back,
                mv_image_left,
                mv_image_right,
                num_steps,
                cfg_scale,
                seed,
                octree_resolution,
                check_box_rembg,
                num_chunks,
                randomize_seed,
            ],
            outputs=[file_out, file_out2, html_gen_mesh, stats, seed],
        ).then(
            lambda: (
                gr.update(visible=True, value=True),
                gr.update(interactive=False),
                gr.update(interactive=True),
                gr.update(interactive=False),
            ),
            outputs=[export_texture, reduce_face, confirm_export, file_export],
        ).then(
            lambda: gr.update(selected="gen_mesh_panel"),
            outputs=[tabs_output],
        )

        def on_gen_mode_change(value):
            if value == "Turbo":
                return gr.update(value=5)
            elif value == "Fast":
                return gr.update(value=10)
            else:
                return gr.update(value=30)

        gen_mode.change(on_gen_mode_change, inputs=[gen_mode], outputs=[num_steps])

        def on_decode_mode_change(value):
            if value == "Low":
                return gr.update(value=196)
            elif value == "Standard":
                return gr.update(value=256)
            else:
                return gr.update(value=384)

        decode_mode.change(
            on_decode_mode_change, inputs=[decode_mode], outputs=[octree_resolution]
        )

        def on_export_click(
            file_out, file_out2, file_type, reduce_face, export_texture, target_face_num
        ):
            if file_out is None:
                raise gr.Error("Please generate a mesh first.")

            print(f"exporting {file_out}")
            print(f"reduce face to {target_face_num}")
            if export_texture:
                mesh = trimesh.load(file_out2)
                save_folder = gen_save_folder()
                path = export_mesh(mesh, save_folder, textured=True, type=file_type)

                # for preview
                save_folder = gen_save_folder()
                _ = export_mesh(mesh, save_folder, textured=True)
                model_viewer_html = build_model_viewer_html(
                    save_folder, height=HTML_HEIGHT, width=HTML_WIDTH, textured=True
                )
            else:
                mesh = trimesh.load(file_out)
                mesh = floater_remove_worker(mesh)
                mesh = degenerate_face_remove_worker(mesh)
                if reduce_face:
                    mesh = face_reduce_worker(mesh, target_face_num)
                save_folder = gen_save_folder()
                path = export_mesh(mesh, save_folder, textured=False, type=file_type)

                # for preview
                save_folder = gen_save_folder()
                _ = export_mesh(mesh, save_folder, textured=False)
                model_viewer_html = build_model_viewer_html(
                    save_folder, height=HTML_HEIGHT, width=HTML_WIDTH, textured=False
                )
            print(f"export to {path}")
            return model_viewer_html, gr.update(value=path, interactive=True)

        confirm_export.click(
            lambda: gr.update(selected="export_mesh_panel"),
            outputs=[tabs_output],
        ).then(
            on_export_click,
            inputs=[
                file_out,
                file_out2,
                file_type,
                reduce_face,
                export_texture,
                target_face_num,
            ],
            outputs=[html_export_mesh, file_export],
        )

    return demo


if __name__ == "__main__":
    import argparse

    parser = argparse.ArgumentParser()
    parser.add_argument("--model_path", type=str, default="tencent/Hunyuan3D-2.1")
    parser.add_argument("--subfolder", type=str, default="hunyuan3d-dit-v2-1")
    parser.add_argument(
        "--texgen_model_path", type=str, default="tencent/Hunyuan3D-2.1"
    )
    parser.add_argument("--port", type=int, default=7860)
    parser.add_argument("--host", type=str, default="0.0.0.0")
    parser.add_argument("--device", type=str, default="cuda")
    parser.add_argument("--mc_algo", type=str, default="mc")
    parser.add_argument("--cache-path", type=str, default="/root/save_dir")
    parser.add_argument("--enable_t23d", action="store_true")
    parser.add_argument("--disable_tex", action="store_true")
    parser.add_argument("--enable_flashvdm", action="store_true")
    parser.add_argument("--compile", action="store_true")
    parser.add_argument("--low_vram_mode", action="store_true")
    args = parser.parse_args()
    args.enable_flashvdm = False

    SAVE_DIR = args.cache_path
    os.makedirs(SAVE_DIR, exist_ok=True)

    CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))
    MV_MODE = "mv" in args.model_path
    TURBO_MODE = "turbo" in args.subfolder

    HTML_HEIGHT = 690 if MV_MODE else 650
    HTML_WIDTH = 500
    HTML_OUTPUT_PLACEHOLDER = f"""
    <div style='height: {650}px; width: 100%; border-radius: 8px; border-color: #e5e7eb; border-style: solid; border-width: 1px; display: flex; justify-content: center; align-items: center;'>
      <div style='text-align: center; font-size: 16px; color: #6b7280;'>
        <p style="color: #8d8d8d;">Welcome to Imagine!</p>
        <p style="color: #8d8d8d;">No mesh here.</p>
      </div>
    </div>
    """

    INPUT_MESH_HTML = """
    <div style='height: 490px; width: 100%; border-radius: 8px; 
    border-color: #e5e7eb; order-style: solid; border-width: 1px;'>
    </div>
    """
    example_is = get_example_img_list()
    example_ts = get_example_txt_list()

    SUPPORTED_FORMATS = ["glb", "obj", "ply", "stl"]

    HAS_TEXTUREGEN = True
    args.disable_tex = False
    if not args.disable_tex:
        try:
            # Apply torchvision fix before importing basicsr/RealESRGAN
            print("Applying torchvision compatibility fix for texture generation...")
            try:
                from torchvision_fix import apply_fix

                fix_result = apply_fix()
                if not fix_result:
                    print(
                        "Warning: Torchvision fix may not have been applied successfully"
                    )
            except Exception as fix_error:
                print(f"Warning: Failed to apply torchvision fix: {fix_error}")

            # from hy3dgen.texgen import Hunyuan3DPaintPipeline
            # texgen_worker = Hunyuan3DPaintPipeline.from_pretrained(args.texgen_model_path)
            # if args.low_vram_mode:
            #     texgen_worker.enable_model_cpu_offload()

            from hy3dpaint.textureGenPipeline import (
                Hunyuan3DPaintPipeline,
                Hunyuan3DPaintConfig,
            )

            conf = Hunyuan3DPaintConfig(max_num_view=8, resolution=768)
            conf.realesrgan_ckpt_path = "hy3dpaint/ckpt/RealESRGAN_x4plus.pth"
            conf.multiview_cfg_path = "hy3dpaint/cfgs/hunyuan-paint-pbr.yaml"
            conf.custom_pipeline = "hy3dpaint/hunyuanpaintpbr"
            tex_pipeline = Hunyuan3DPaintPipeline(conf)

            # Not help much, ignore for now.
            # if args.compile:
            #     texgen_worker.models['delight_model'].pipeline.unet.compile()
            #     texgen_worker.models['delight_model'].pipeline.vae.compile()
            #     texgen_worker.models['multiview_model'].pipeline.unet.compile()
            #     texgen_worker.models['multiview_model'].pipeline.vae.compile()

            HAS_TEXTUREGEN = True

        except Exception as e:
            print(f"Error loading texture generator: {e}")
            print("Failed to load texture generator.")
            print("Please try to install requirements by following README.md")
            HAS_TEXTUREGEN = False

    # HAS_T2I = True
    # if args.enable_t23d:
    #     from hy3dgen.text2image import HunyuanDiTPipeline

    #     t2i_worker = HunyuanDiTPipeline('Tencent-Hunyuan/HunyuanDiT-v1.1-Diffusers-Distilled')
    #     HAS_T2I = True

    from hy3dshape import (
        FaceReducer,
        FloaterRemover,
        DegenerateFaceRemover,
        MeshSimplifier,
        Hunyuan3DDiTFlowMatchingPipeline,
    )
    from hy3dshape.pipelines import export_to_trimesh
    from hy3dshape.rembg import BackgroundRemover

    rmbg_worker = BackgroundRemover()
    i23d_worker = Hunyuan3DDiTFlowMatchingPipeline.from_pretrained(
        args.model_path,
        subfolder=args.subfolder,
        use_safetensors=False,
        device=args.device,
    )
    if args.enable_flashvdm:
        mc_algo = "mc" if args.device in ["cpu", "mps"] else args.mc_algo
        i23d_worker.enable_flashvdm(mc_algo=mc_algo)
    if args.compile:
        i23d_worker.compile()

    floater_remove_worker = FloaterRemover()
    degenerate_face_remove_worker = DegenerateFaceRemover()
    face_reduce_worker = FaceReducer()

    # https://discuss.huggingface.co/t/how-to-serve-an-html-file/33921/2
    # create a FastAPI app
    app = FastAPI()

    # create a static directory to store the static files
    static_dir = Path(SAVE_DIR).absolute()
    static_dir.mkdir(parents=True, exist_ok=True)
    app.mount("/static", StaticFiles(directory=static_dir, html=True), name="static")
    shutil.copytree(
        "./assets/env_maps", os.path.join(static_dir, "env_maps"), dirs_exist_ok=True
    )

    if args.low_vram_mode:
        torch.cuda.empty_cache()

    demo = build_app()
    app = gr.mount_gradio_app(app, demo, path="/")

    if ENV == "Huggingface":
        # for Zerogpu
        from spaces import zero

        zero.startup()

    uvicorn.run(app, host=args.host, port=args.port)