import os if 'OMP_NUM_THREADS' not in os.environ: os.environ['OMP_NUM_THREADS'] = '16' import torch import subprocess import gradio as gr from functools import partial from huggingface_hub import snapshot_download from freesplatter.webui.runner import FreeSplatterRunner from freesplatter.webui.tab_img_to_3d import create_interface_img_to_3d def install_cuda_toolkit(): CUDA_TOOLKIT_URL = "https://developer.download.nvidia.com/compute/cuda/12.1.0/local_installers/cuda_12.1.0_530.30.02_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() torch.set_grad_enabled(False) device = torch.device('cuda') runner = FreeSplatterRunner(device) _HEADER_ = ''' # FreeSplatter 🤗 Gradio Demo \n\nOfficial demo of the paper [FreeSplatter: Pose-free Gaussian Splatting for Sparse-view 3D Reconstruction](https://arxiv.org/abs/2404.07191). [[Github]](https://github.com/TencentARC/FreeSplatter) **FreeSplatter** is a feed-forward framework capable of generating high-quality 3D Gaussians from **uncalibrated** sparse-view images and recovering their camera parameters in mere seconds. ''' _IMG_TO_3D_HELP_ = ''' 💡💡💡**Usage Tips:** - This demo supports various multi-view diffusion models, including [Hunyuan3D](https://github.com/Tencent/Hunyuan3D-1) Std and [Zero123++](https://github.com/SUDO-AI-3D/zero123plus) v1.1/v1.2. You can try different models to get the best result. - Try clicking the \U0001f3b2\ufe0f button to use a different `Random seed` (default: 42) for diverse outputs. - In most cases, using `2DGS` leads to better mesh geometry than `3DGS`. Please refer to the [2DGS paper](https://arxiv.org/abs/2403.17888). - You can adjust the views used for reconstruction to alleviate the blurry texture problem caused by multi-view inconsistency. ❗️❗️❗️**NOTE:** We are dealing with some bugs related to the ZeroGPU environment, stay tuned! ''' _CITE_ = r""" If FreeSplatter is helpful, please help to ⭐ the Github Repo. Thanks! [![GitHub Stars](https://img.shields.io/github/stars/TencentARC/FreeSplatter?style=social)](https://github.com/TencentARC/FreeSplatter) --- 📝 **Citation** If you find our work useful for your research or applications, please cite using this bibtex: ```bibtex @article{xu2024freesplatter, title={FreeSplatter: Pose-free Gaussian Splatting for Sparse-view 3D Reconstruction}, author={Xu, Jiale and Gao, Shenghua and Shan, Ying}, journal={arXiv preprint}, year={2024} } ``` 📋 **License** Apache-2.0 LICENSE. Please refer to the [LICENSE file](https://huggingface.co/spaces/TencentARC/FreeSplatter/blob/main/LICENSE) for details. 📧 **Contact** If you have any questions, feel free to open a discussion or contact us at bluestyle928@gmail.com. """ with gr.Blocks(analytics_enabled=False, title='FreeSplatter Demo') as demo: gr.Markdown(_HEADER_) with gr.Tabs() as main_tabs: with gr.TabItem('Image-to-3D', id='tab_img_to_3d'): gr.Markdown(_IMG_TO_3D_HELP_) with gr.Tabs() as sub_tabs_img_to_3d: with gr.TabItem('Hunyuan3D Std', id='tab_hunyuan3d_std'): _, var_img_to_3d_hunyuan3d_std = create_interface_img_to_3d( runner.run_segmentation, runner.run_img_to_3d, model='Hunyuan3D Std') with gr.TabItem('Zero123++ v1.1', id='tab_zero123plus_v11'): _, var_img_to_3d_zero123plus_v11 = create_interface_img_to_3d( runner.run_segmentation, runner.run_img_to_3d, model='Zero123++ v1.1') with gr.TabItem('Zero123++ v1.2', id='tab_zero123plus_v12'): _, var_img_to_3d_zero123plus_v12 = create_interface_img_to_3d( runner.run_segmentation, runner.run_img_to_3d, model='Zero123++ v1.2') gr.Markdown(_CITE_) demo.launch()