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hg_app_bak.py

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+# pip install gradio==4.44.1
+if True:
+    import os
+    import spaces
+    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)    
+    IP = "0.0.0.0"
+    PORT = 7860
+else:
+    IP = "0.0.0.0"
+    PORT = 8080
+    class spaces:
+        class GPU:
+            def __init__(self, duration=60):
+                self.duration = duration
+            def __call__(self, func):
+                return func 
+
+import os
+import shutil
+import time
+from glob import glob
+import gradio as gr
+import torch
+from gradio_litmodel3d import LitModel3D
+
+
+def get_example_img_list():
+    print('Loading example img list ...')
+    return sorted(glob('./assets/example_images/*.png'))
+
+
+def get_example_txt_list():
+    print('Loading example txt list ...')
+    txt_list = list()
+    for line in open('./assets/example_prompts.txt'):
+        txt_list.append(line.strip())
+    return txt_list
+
+
+def gen_save_folder(max_size=6000):
+    os.makedirs(SAVE_DIR, exist_ok=True)
+    exists = set(int(_) for _ in os.listdir(SAVE_DIR) if not _.startswith("."))
+    cur_id = min(set(range(max_size)) - exists) if len(exists) < max_size else -1
+    if os.path.exists(f"{SAVE_DIR}/{(cur_id + 1) % max_size}"):
+        shutil.rmtree(f"{SAVE_DIR}/{(cur_id + 1) % max_size}")
+        print(f"remove {SAVE_DIR}/{(cur_id + 1) % max_size} success !!!")
+    save_folder = f"{SAVE_DIR}/{max(0, cur_id)}"
+    os.makedirs(save_folder, exist_ok=True)
+    print(f"mkdir {save_folder} suceess !!!")
+    return save_folder
+
+
+def export_mesh(mesh, save_folder, textured=False):
+    if textured:
+        path = os.path.join(save_folder, f'textured_mesh.glb')
+    else:
+        path = os.path.join(save_folder, f'white_mesh.glb')
+    mesh.export(path, include_normals=textured)
+    return path
+
+
+def build_model_viewer_html(save_folder, height=660, width=790, textured=False):
+    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')
+
+    with open(os.path.join(CURRENT_DIR, template_name), 'r') as f:
+        template_html = f.read()
+        obj_html = f"""
+            <div class="column is-mobile is-centered">
+                <model-viewer style="height: {height - 10}px; width: {width}px;" rotation-per-second="10deg" id="modelViewer"
+                    src="{related_path}/" disable-tap 
+                    environment-image="neutral" auto-rotate camera-target="0m 0m 0m" orientation="0deg 0deg 170deg" shadow-intensity=".9"
+                    ar auto-rotate camera-controls>
+                </model-viewer>
+            </div>
+            """
+
+    with open(output_html_path, 'w') as f:
+        f.write(template_html.replace('<model-viewer>', obj_html))
+
+    iframe_tag = f'<iframe src="file/{output_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>
+    """
+
+@spaces.GPU(duration=60)
+def _gen_shape(
+    caption,
+    image,
+    steps=50,
+    guidance_scale=7.5,
+    seed=1234,
+    octree_resolution=256,
+    check_box_rembg=False,
+):
+    if caption: print('prompt is', caption)
+    save_folder = gen_save_folder()
+    stats = {}
+    time_meta = {}
+    start_time_0 = time.time()
+
+    image_path = ''
+    if image is None:
+        start_time = time.time()
+        image = t2i_worker(caption)
+        time_meta['text2image'] = time.time() - start_time
+
+    image.save(os.path.join(save_folder, 'input.png'))
+
+    print(image.mode)
+    if check_box_rembg or image.mode == "RGB":
+        start_time = time.time()
+        image = rmbg_worker(image.convert('RGB'))
+        time_meta['rembg'] = time.time() - start_time
+
+    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))
+    mesh = i23d_worker(
+        image=image,
+        num_inference_steps=steps,
+        guidance_scale=guidance_scale,
+        generator=generator,
+        octree_resolution=octree_resolution
+    )[0]
+
+    mesh = FloaterRemover()(mesh)
+    mesh = DegenerateFaceRemover()(mesh)
+    mesh = FaceReducer()(mesh)
+
+    stats['number_of_faces'] = mesh.faces.shape[0]
+    stats['number_of_vertices'] = mesh.vertices.shape[0]
+
+    time_meta['image_to_textured_3d'] = {'total': time.time() - start_time}
+    time_meta['total'] = time.time() - start_time_0
+    stats['time'] = time_meta
+    return mesh, save_folder, image
+
+@spaces.GPU(duration=80)
+def generation_all(
+    caption,
+    image,
+    steps=50,
+    guidance_scale=7.5,
+    seed=1234,
+    octree_resolution=256,
+    check_box_rembg=False
+):
+    mesh, save_folder, image = _gen_shape(
+        caption,
+        image,
+        steps=steps,
+        guidance_scale=guidance_scale,
+        seed=seed,
+        octree_resolution=octree_resolution,
+        check_box_rembg=check_box_rembg
+    )
+    path = export_mesh(mesh, save_folder, textured=False)
+    model_viewer_html = build_model_viewer_html(save_folder, height=596, width=700)
+
+    textured_mesh = texgen_worker(mesh, image)
+    path_textured = export_mesh(textured_mesh, save_folder, textured=True)
+    model_viewer_html_textured = build_model_viewer_html(save_folder, height=596, width=700, textured=True)
+
+    return (
+        gr.update(value=path, visible=True),
+        gr.update(value=path_textured, visible=True),
+        gr.update(value=path, visible=True),
+        gr.update(value=path_textured, visible=True),
+        # model_viewer_html,
+        # model_viewer_html_textured,
+    )
+
+@spaces.GPU(duration=30)
+def shape_generation(
+    caption,
+    image,
+    steps=50,
+    guidance_scale=7.5,
+    seed=1234,
+    octree_resolution=256,
+    check_box_rembg=False,
+):
+    mesh, save_folder, image = _gen_shape(
+        caption,
+        image,
+        steps=steps,
+        guidance_scale=guidance_scale,
+        seed=seed,
+        octree_resolution=octree_resolution,
+        check_box_rembg=check_box_rembg
+    )
+
+    path = export_mesh(mesh, save_folder, textured=False)
+    model_viewer_html = build_model_viewer_html(save_folder, height=596, width=700)
+
+    return (
+        gr.update(value=path, visible=True),
+        gr.update(value=path, visible=True),
+        # model_viewer_html,
+    )
+
+
+def build_app():
+    title_html = """
+    <div style="font-size: 2em; font-weight: bold; text-align: center; margin-bottom: 20px">
+    
+    Hunyuan3D-2: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation
+    </div>
+    <div align="center">
+    Tencent Hunyuan3D Team
+    </div>
+    <div align="center">
+      <a href="https://github.com/tencent/Hunyuan3D-1">Github Page</a> &ensp; 
+      <a href="http://3d-models.hunyuan.tencent.com">Homepage</a> &ensp;
+      <a href="https://arxiv.org/pdf/2411.02293">Technical Report</a> &ensp;
+      <a href="https://huggingface.co/Tencent/Hunyuan3D-2"> Models</a> &ensp;
+    </div>
+    """
+    css = """
+    .json-output {
+        height: 578px;
+    }
+    .json-output .json-holder {
+        height: 538px;
+        overflow-y: scroll;
+    }
+    """
+
+    with gr.Blocks(theme=gr.themes.Base(), css=css, title='Hunyuan-3D-2.0') as demo:
+        # if not gr.__version__.startswith('4'): gr.HTML(title_html)
+        gr.HTML(title_html)
+
+        with gr.Row():
+            with gr.Column(scale=2):
+                with gr.Tabs() as tabs_prompt:
+                    with gr.Tab('Image Prompt', id='tab_img_prompt') as tab_ip:
+                        image = gr.Image(label='Image', type='pil', image_mode='RGBA', height=290)
+                        with gr.Row():
+                            check_box_rembg = gr.Checkbox(value=True, label='Remove Background')
+
+                    with gr.Tab('Text Prompt', id='tab_txt_prompt') 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.Accordion('Advanced Options', open=False):
+                    num_steps = gr.Slider(maximum=50, minimum=20, value=30, step=1, label='Inference Steps')
+                    octree_resolution = gr.Dropdown([256, 384, 512], value=256, label='Octree Resolution')
+                    cfg_scale = gr.Number(value=5.5, label='Guidance Scale')
+                    seed = gr.Slider(maximum=1e7, minimum=0, value=1234, label='Seed')
+
+                with gr.Group():
+                    btn = gr.Button(value='Generate Shape Only', variant='primary')
+                    btn_all = gr.Button(value='Generate Shape and Texture', variant='primary')
+
+                with gr.Group():
+                    file_out = gr.File(label="File", visible=False)
+                    file_out2 = gr.File(label="File", visible=False)
+
+            with gr.Column(scale=5):
+                with gr.Tabs():
+                    with gr.Tab('Generated Mesh') as mesh1:
+                        mesh_output1 = LitModel3D(
+                            label="3D Model1",
+                            exposure=10.0,
+                            height=600,
+                            visible=True,
+                            clear_color=[0.0, 0.0, 0.0, 0.0],
+                            tonemapping="aces",
+                            contrast=1.0,
+                            scale=1.0,
+                        )
+                        # html_output1 = gr.HTML(HTML_OUTPUT_PLACEHOLDER, label='Output')
+                    with gr.Tab('Generated Textured Mesh') as mesh2:
+                        # html_output2 = gr.HTML(HTML_OUTPUT_PLACEHOLDER, label='Output')
+                        mesh_output2 = LitModel3D(
+                            label="3D Model2",
+                            exposure=10.0,
+                            height=600,
+                            visible=True,
+                            clear_color=[0.0, 0.0, 0.0, 0.0],
+                            tonemapping="aces",
+                            contrast=1.0,
+                            scale=1.0,
+                        )
+
+            with gr.Column(scale=2):
+                with gr.Tabs() as gallery:
+                    with gr.Tab('Image to 3D Gallery', id='tab_img_gallery') as tab_gi:
+                        with gr.Row():
+                            gr.Examples(examples=example_is, inputs=[image],
+                                        label="Image Prompts", examples_per_page=18)
+
+                    with gr.Tab('Text to 3D Gallery', id='tab_txt_gallery') as tab_gt:
+                        with gr.Row():
+                            gr.Examples(examples=example_ts, inputs=[caption],
+                                        label="Text Prompts", examples_per_page=18)
+
+        tab_gi.select(fn=lambda: gr.update(selected='tab_img_prompt'), outputs=tabs_prompt)
+        tab_gt.select(fn=lambda: gr.update(selected='tab_txt_prompt'), outputs=tabs_prompt)
+
+        btn.click(
+            shape_generation,
+            inputs=[
+                caption,
+                image,
+                num_steps,
+                cfg_scale,
+                seed,
+                octree_resolution,
+                check_box_rembg,
+            ],
+            # outputs=[file_out, html_output1]
+            outputs=[file_out, mesh_output1]
+        ).then(
+            lambda: gr.update(visible=True),
+            outputs=[file_out],
+        )
+
+        btn_all.click(
+            generation_all,
+            inputs=[
+                caption,
+                image,
+                num_steps,
+                cfg_scale,
+                seed,
+                octree_resolution,
+                check_box_rembg,
+            ],
+            # outputs=[file_out, file_out2, html_output1, html_output2]
+            outputs=[file_out, file_out2, mesh_output1, mesh_output2]
+        ).then(
+            lambda: (gr.update(visible=True), gr.update(visible=True)),
+            outputs=[file_out, file_out2],
+        )
+
+    return demo
+
+
+if __name__ == '__main__':
+    import argparse
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--port', type=int, default=8080)
+    parser.add_argument('--cache-path', type=str, default='./gradio_cache')
+    args = parser.parse_args()
+
+    SAVE_DIR = args.cache_path
+    os.makedirs(SAVE_DIR, exist_ok=True)
+
+    CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))
+
+    HTML_OUTPUT_PLACEHOLDER = """
+    <div style='height: 596px; width: 100%; border-radius: 8px; border-color: #e5e7eb; order-style: solid; border-width: 1px;'></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()
+
+    from hy3dgen.text2image import HunyuanDiTPipeline
+    from hy3dgen.shapegen import FaceReducer, FloaterRemover, DegenerateFaceRemover, \
+        Hunyuan3DDiTFlowMatchingPipeline
+    from hy3dgen.texgen import Hunyuan3DPaintPipeline
+    from hy3dgen.rembg import BackgroundRemover
+
+    rmbg_worker = BackgroundRemover()
+    t2i_worker = HunyuanDiTPipeline()
+    i23d_worker = Hunyuan3DDiTFlowMatchingPipeline.from_pretrained('tencent/Hunyuan3D-2')
+    texgen_worker = Hunyuan3DPaintPipeline.from_pretrained('tencent/Hunyuan3D-2')
+    floater_remove_worker = FloaterRemover()
+    degenerate_face_remove_worker = DegenerateFaceRemover()
+    face_reduce_worker = FaceReducer()
+
+    demo = build_app()
+    demo.queue().launch(server_name=IP,server_port=PORT)