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.

import os
import random
import shutil
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
from mmgp import offload
import uuid

from hy3dgen.shapegen.utils import logger

MAX_SEED = 1e7


def get_example_img_list():
    print('Loading example img list ...')
    return sorted(glob('./assets/example_images/**/*.png', recursive=True))


def get_example_txt_list():
    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 get_example_mv_list():
    print('Loading example mv list ...')
    mv_list = list()
    root = './assets/example_mv_images'
    for mv_dir in os.listdir(root):
        view_list = []
        for view in ['front', 'back', 'left', 'right']:
            path = os.path.join(root, mv_dir, f'{view}.png')
            if os.path.exists(path):
                view_list.append(path)
            else:
                view_list.append(None)
        mv_list.append(view_list)
    return mv_list


def gen_save_folder(max_size=200):
    os.makedirs(SAVE_DIR, exist_ok=True)

    # 获取所有文件夹路径
    dirs = [f for f in Path(SAVE_DIR).iterdir() if f.is_dir()]

    # 如果文件夹数量超过 max_size，删除创建时间最久的文件夹
    if len(dirs) >= max_size:
        # 按创建时间排序，最久的排在前面
        oldest_dir = min(dirs, key=lambda x: x.stat().st_ctime)
        shutil.rmtree(oldest_dir)
        print(f"Removed the oldest folder: {oldest_dir}")

    # 生成一个新的 uuid 文件夹名称
    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


def export_mesh(mesh, save_folder, textured=False, type='glb'):
    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 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}')

    # html_path = '/'.join(Path(output_html_path).parts[1:])
    # iframe_tag = f'<iframe src="/static/{html_path}" height="{height}" width="100%" frameborder="0"></iframe>'
    # print(f'Find html {output_html_path}, {os.path.exists(output_html_path)}')

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


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


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)

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

    tmp_time = time.time()
    textured_mesh = texgen_worker(mesh, image)
    logger.info("---Texture Generation takes %s seconds ---" % (time.time() - tmp_time))
    stats['time']['texture generation'] = time.time() - tmp_time
    stats['time']['total'] = time.time() - start_time_0

    textured_mesh.metadata['extras'] = stats
    path_textured = export_mesh(textured_mesh, save_folder, textured=True)
    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=path_textured),
        model_viewer_html_textured,
        stats,
        seed,
    )


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,
    )
    path = export_mesh(mesh, save_folder, textured=False)

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

    tmp_time = time.time()
    textured_mesh = texgen_worker(mesh, image)
    logger.info("---Texture Generation takes %s seconds ---" % (time.time() - tmp_time))
    stats['time']['texture generation'] = time.time() - tmp_time
    stats['time']['total'] = time.time() - start_time_0

    textured_mesh.metadata['extras'] = stats
    path_textured = export_mesh(textured_mesh, save_folder, textured=True)
    model_viewer_html_textured = build_model_viewer_html(save_folder, height=HTML_HEIGHT, width=HTML_WIDTH,
                                                         textured=True)
    torch.cuda.empty_cache()
    return (
        gr.update(value=path),
        gr.update(value=path_textured),
        model_viewer_html_textured,
        stats,
        seed,
    )


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():

    if MV_MODE:
        title = 'Hunyuan3D-2mv<SUP>GP</SUP>: Image to 3D Generation with 1-4 Views'
    elif 'mini' in args.subfolder:
        title = 'Hunyuan3D-2mini<SUP>GP</SUP>: Strong 0.6B Image to Shape Generator'
    else:
        title = 'Hunyuan3D-2<SUP>GP</SUP>'
        title = 'Hunyuan3D-2mv: Image to 3D Generation with 1-4 Views'
    if 'mini' in args.subfolder:
        title = 'Hunyuan3D-2mini: Strong 0.6B Image to Shape Generator'
    if TURBO_MODE:
        title = title.replace(':', '-Turbo: Fast ')

    title_html = f"""<H1>
    <div align=center>{title}</div></H1>
    <BR>
    <div align="center"><FONT SIZE=4><B>Original model by Tencent, GPU Poor version by DeepBeepMeep. Now this great 3D video generator can run smoothly with a 6 GB rig.</B></FONT>
    </DIV>
    <BR>
    <div align="center"> H3D-2GP : <a href="https://github.com/deepbeepmeep/Hunyuan3D-2GP">Updates</a>, Original by Tencent Hunyuan3D Team -
      <a href="https://github.com/tencent/Hunyuan3D-2">Github Page</a> &ensp; 
    """
    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='Hunyuan-3D-2.0', 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)

                    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)

                    with gr.Tab('Text to 3D Gallery', id='tab_txt_gallery', visible=HAS_T2I and not MV_MODE) as tab_gt:
                        with gr.Row():
                            gr.Examples(examples=example_ts, inputs=[caption],
                                        label=None, examples_per_page=18)
                    with gr.Tab('MultiView to 3D Gallery', id='tab_mv_gallery', visible=MV_MODE) as tab_mv:
                        with gr.Row():
                            gr.Examples(examples=example_mvs,
                                        inputs=[mv_image_front, mv_image_back, mv_image_left, mv_image_right],
                                        label=None, examples_per_page=6)

        gr.HTML(f"""
        <div align="center">
        Activated Model - Shape Generation ({args.model_path}/{args.subfolder}) ; Texture Generation ({'Hunyuan3D-2' if HAS_TEXTUREGEN else 'Unavailable'})
        </div>
        """)

        if not HAS_TEXTUREGEN:
            gr.HTML("""
            <div style="margin-top: 5px;"  align="center">
                <b>Warning: </b>
                Texture synthesis is disable due to missing requirements,
                 please install requirements following <a href="https://github.com/Tencent/Hunyuan3D-2?tab=readme-ov-file#install-requirements">README.md</a>to activate it.
            </div>
            """)
        if not args.enable_t23d:
            gr.HTML("""
            <div style="margin-top: 5px;"  align="center">
                <b>Warning: </b>
                Text to 3D is disable. To activate it, please run `python gradio_app.py --enable_t23d`.
            </div>
            """)

        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

def replace_property_getter(instance, property_name, new_getter):
    # Get the original class and property
    original_class = type(instance)
    original_property = getattr(original_class, property_name)
    
    # Create a custom subclass for this instance
    custom_class = type(f'Custom{original_class.__name__}', (original_class,), {})
    
    # Create a new property with the new getter but same setter
    new_property = property(new_getter, original_property.fset)
    setattr(custom_class, property_name, new_property)
    
    # Change the instance's class
    instance.__class__ = custom_class
    
    return instance

if __name__ == '__main__':
    import argparse

    parser = argparse.ArgumentParser()
    parser.add_argument("--model_path", type=str, default='tencent/Hunyuan3D-2mini')
    parser.add_argument("--subfolder", type=str, default='hunyuan3d-dit-v2-mini')
    parser.add_argument("--texgen_model_path", type=str, default='tencent/Hunyuan3D-2')
    parser.add_argument('--port', type=int, default=8080)
    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='dmc')
    parser.add_argument('--cache-path', type=str, default='gradio_cache')
    parser.add_argument('--enable_t23d', action='store_true')
    parser.add_argument('--profile', type=str, default="3")
    parser.add_argument('--verbose', type=str, default="1")

    parser.add_argument('--disable_tex', action='store_true')
    parser.add_argument('--enable_flashvdm', action='store_true')
    parser.add_argument('--low-vram-mode', action='store_true')
    parser.add_argument('--compile', action='store_true')
    parser.add_argument('--mini', action='store_true')
    parser.add_argument('--turbo', action='store_true')
    parser.add_argument('--mv', action='store_true')
    parser.add_argument('--h2', action='store_true')


    args = parser.parse_args()

    if args.mini:
        args.model_path = "tencent/Hunyuan3D-2mini"
        args.subfolder=  "hunyuan3d-dit-v2-mini"
        args.texgen_model_path = "tencent/Hunyuan3D-2"

    if args.mv:
        args.model_path = "tencent/Hunyuan3D-2mv"
        args.subfolder=  "hunyuan3d-dit-v2-mv"
        args.texgen_model_path = "tencent/Hunyuan3D-2"


    if args.h2:
        args.model_path = "tencent/Hunyuan3D-2"
        args.subfolder=  "hunyuan3d-dit-v2-0"
        args.texgen_model_path = "tencent/Hunyuan3D-2"

    if args.turbo:
        args.subfolder= args.subfolder  + "-turbo"
        args.enable_flashvdm = True

    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 Hunyuan3D!</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()
    torch.set_default_device("cpu")
    # try:
    #     from hy3dgen.texgen import Hunyuan3DPaintPipeline
    example_mvs = get_example_mv_list()

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

    HAS_TEXTUREGEN = False
    if not args.disable_tex:
        try:
            from hy3dgen.texgen import Hunyuan3DPaintPipeline

            texgen_worker = Hunyuan3DPaintPipeline.from_pretrained(args.texgen_model_path)
            # texgen_worker.enable_model_cpu_offload()
            # 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(e)
            print("Failed to load texture generator.")
            print('Please try to install requirements by following README.md')
            HAS_TEXTUREGEN = False

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

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

    from hy3dgen.shapegen import FaceReducer, FloaterRemover, DegenerateFaceRemover, MeshSimplifier, \
        Hunyuan3DDiTFlowMatchingPipeline
    from hy3dgen.shapegen.pipelines import export_to_trimesh
    from hy3dgen.rembg import BackgroundRemover

    rmbg_worker = BackgroundRemover()
    i23d_worker = Hunyuan3DDiTFlowMatchingPipeline.from_pretrained(
        args.model_path,
        subfolder=args.subfolder,
        use_safetensors=True,
        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()
  
    profile = int(args.profile) 
    kwargs = {}
    replace_property_getter(i23d_worker, "_execution_device", lambda self : "cuda")
    pipe = offload.extract_models("i23d_worker", i23d_worker)
    if HAS_TEXTUREGEN:
        pipe.update(  offload.extract_models( "texgen_worker", texgen_worker))
        texgen_worker.models["multiview_model"].pipeline.vae.use_slicing = True
    if HAS_T2I:
        pipe.update(  offload.extract_models( "t2i_worker", t2i_worker))
        

    if profile < 5:
        kwargs["pinnedMemory"] = "i23d_worker/model"
    if profile !=1 and profile !=3:
        kwargs["budgets"] = { "*" : 2200 }
    offload.default_verboseLevel = verboseLevel = int(args.verbose)
    offload.profile(pipe, profile_no = profile, verboseLevel = int(args.verbose), **kwargs)


    # 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="/")
    uvicorn.run(app, host=args.host, port=args.port, workers=1)