Upload gradio_app.py

hy3dgen/texgen/differentiable_renderer/gradio_app.py

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+# 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
+import uuid
+
+from hy3dgen.shapegen.utils import logger
+
+MAX_SEED = 1e7
+
+import spaces
+
+if True:
+    import os    
+    import subprocess
+    import sys
+    import shlex
+    print("cd /home/user/app/hy3dgen/texgen/differentiable_renderer/ && bash compile_mesh_painter.sh")
+    os.system("cd /home/user/app/hy3dgen/texgen/differentiable_renderer/ && bash compile_mesh_painter.sh")
+    print('install custom')
+    subprocess.run(shlex.split("pip install custom_rasterizer-0.1-cp310-cp310-linux_x86_64.whl"), check=True)
+
+# 2 functions just for GPU usage at startup
+@spaces.GPU
+def my_gpu_function():
+    return "my_gpu_function processed on GPU"
+
+def call_gpu_function():
+    return my_gpu_function()  # Only called on Gradio event
+
+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}')
+
+    return f"""
+        <div style='height: {height}; width: 100%;'>
+        {iframe_tag}
+        </div>
+    """
+
+@spaces.GPU(duration=40)
+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=90)
+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,
+    )
+
+@spaces.GPU(duration=40)
+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 = 'Hunyuan3D-2: High Resolution Textured 3D Assets Generation'
+    if MV_MODE:
+        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"""
+    <div style="font-size: 2em; font-weight: bold; text-align: center; margin-bottom: 5px">
+
+    {title}
+    </div>
+    <div align="center">
+    Tencent Hunyuan3D Team
+    </div>
+    <div align="center">
+      <a href="https://github.com/tencent/Hunyuan3D-2">Github</a> &ensp; 
+      <a href="http://3d-models.hunyuan.tencent.com">Homepage</a> &ensp;
+      <a href="https://3d.hunyuan.tencent.com">Hunyuan3D Studio</a> &ensp;
+      <a href="#">Technical Report</a> &ensp;
+      <a href="https://huggingface.co/Tencent/Hunyuan3D-2"> Pretrained Models</a> &ensp;
+    </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='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
+
+
+if __name__ == '__main__':
+    import sys
+    print("sys.argv:", sys.argv)
+
+    import argparse
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--model_path", type=str, default='tencent/Hunyuan3D-2mv')
+    parser.add_argument("--subfolder", type=str, default='hunyuan3d-dit-v2-mv-turbo')
+    parser.add_argument("--texgen_model_path", type=str, default='tencent/Hunyuan3D-2')
+    #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='gradio_cache')
+    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()
+
+    try:
+        port = int(args.port)
+    except ValueError:
+        print(f"Invalid port argument detected: {args.port} — using default 7860")
+        port = 7860
+
+    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>
+    """
+
+    gpu_usage = gr.Interface(fn=call_gpu_function, inputs=[], outputs="text")
+    gpu_usage.launch()
+
+    example_is = get_example_img_list()
+    example_ts = get_example_txt_list()
+    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)
+            if args.low_vram_mode:
+                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 = True
+    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()
+
+    # 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)