# 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 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) 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'' print( f'Find html file {output_html_path}, {os.path.exists(output_html_path)}, relative HTML path is /static/{rel_path}') return f"""
{iframe_tag}
""" @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"""
{title}
Tencent Hunyuan3D Team
Github   Homepage   Hunyuan3D Studio   Technical Report   Pretrained Models  
""" 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"""
Activated Model - Shape Generation ({args.model_path}/{args.subfolder}) ; Texture Generation ({'Hunyuan3D-2' if HAS_TEXTUREGEN else 'Unavailable'})
""") if not HAS_TEXTUREGEN: gr.HTML("""
Warning: Texture synthesis is disable due to missing requirements, please install requirements following README.mdto activate it.
""") if not args.enable_t23d: gr.HTML("""
Warning: Text to 3D is disable. To activate it, please run `python gradio_app.py --enable_t23d`.
""") 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-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() 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"""

Welcome to Hunyuan3D!

No mesh here.

""" INPUT_MESH_HTML = """
""" 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)