# pip install gradio==4.44.1 if False: import os import spaces import subprocess def install_cuda_toolkit(): # CUDA_TOOLKIT_URL = "https://developer.download.nvidia.com/compute/cuda/11.8.0/local_installers/cuda_11.8.0_520.61.05_linux.run" CUDA_TOOLKIT_URL = "https://developer.download.nvidia.com/compute/cuda/12.2.0/local_installers/cuda_12.2.0_535.54.03_linux.run" CUDA_TOOLKIT_FILE = "/tmp/%s" % os.path.basename(CUDA_TOOLKIT_URL) subprocess.call(["wget", "-q", CUDA_TOOLKIT_URL, "-O", CUDA_TOOLKIT_FILE]) subprocess.call(["chmod", "+x", CUDA_TOOLKIT_FILE]) subprocess.call([CUDA_TOOLKIT_FILE, "--silent", "--toolkit"]) os.environ["CUDA_HOME"] = "/usr/local/cuda" os.environ["PATH"] = "%s/bin:%s" % (os.environ["CUDA_HOME"], os.environ["PATH"]) os.environ["LD_LIBRARY_PATH"] = "%s/lib:%s" % ( os.environ["CUDA_HOME"], "" if "LD_LIBRARY_PATH" not in os.environ else os.environ["LD_LIBRARY_PATH"], ) # Fix: arch_list[-1] += '+PTX'; IndexError: list index out of range os.environ["TORCH_CUDA_ARCH_LIST"] = "8.0;8.6" install_cuda_toolkit() os.system("cd /home/user/app/hy3dgen/texgen/differentiable_renderer/ && bash compile_mesh_painter.sh") os.system("cd /home/user/app/hy3dgen/texgen/custom_rasterizer && pip install .") # os.system("cd /home/user/app/hy3dgen/texgen/custom_rasterizer && CUDA_HOME=/usr/local/cuda FORCE_CUDA=1 TORCH_CUDA_ARCH_LIST='8.0;8.6;8.9;9.0' python setup.py install") else: 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=60): 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"""
""" with open(output_html_path, 'w') as f: f.write(template_html.replace('', obj_html)) iframe_tag = f'' print(f'Find html {output_html_path}, {os.path.exists(output_html_path)}') return f"""
{iframe_tag}
""" @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 @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 = _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 = _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 = """
Hunyuan3D-2: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation
Tencent Hunyuan3D Team
Github PageHomepageTechnical Report Models
""" 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 = """
""" INPUT_MESH_HTML = """
""" 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()