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Chao Xu
commited on
Commit
β’
1d24bdc
1
Parent(s):
22945de
support .glb export and update README
Browse files
README.md
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---
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title: One
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emoji:
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colorFrom: red
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colorTo: yellow
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sdk: gradio
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sdk_version: 3.36.1
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app_file: app.py
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pinned:
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license: mit
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---
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---
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title: One-2-3-45
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emoji: πΈππ
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colorFrom: red
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colorTo: yellow
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sdk: gradio
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sdk_version: 3.36.1
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app_file: app.py
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pinned: true
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license: mit
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---
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# One-2-3-45: Any Single Image to 3D Mesh in 45 Seconds without Per-Shape Optimization
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Paper: https://arxiv.org/abs/2306.16928
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Code: https://github.com/One-2-3-45/One-2-3-45
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## BibTeX
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```bibtex
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@misc{liu2023one2345,
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title={One-2-3-45: Any Single Image to 3D Mesh in 45 Seconds without Per-Shape Optimization},
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author={Minghua Liu and Chao Xu and Haian Jin and Linghao Chen and Mukund Varma T and Zexiang Xu and Hao Su},
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year={2023},
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eprint={2306.16928},
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archivePrefix={arXiv},
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primaryClass={cs.CV}
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}
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```
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app.py
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@@ -348,7 +348,7 @@ def stage1_run(models, device, cam_vis, tmp_dir,
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return (rerun_all, *reset, *outputs)
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def stage2_run(models, device, tmp_dir,
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elev, scale, rerun_all=[], stage2_steps=50):
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flag_lower_cam = 90-int(elev["label"]) <= 75
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is_rerun = True if rerun_all else False
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model = models['turncam'].half()
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os.chdir(main_dir_path)
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ply_path = os.path.join(tmp_dir, f"meshes_val_bg/lod0/mesh_00215000_gradio_lod0.ply")
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# Read the textured mesh from .ply file
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mesh = trimesh.load_mesh(ply_path)
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axis = [1, 0, 0]
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mesh.vertices[:, 0] = -mesh.vertices[:, 0]
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mesh.faces = np.fliplr(mesh.faces)
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# Export the mesh as .obj file with colors
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torch.cuda.empty_cache()
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if not is_rerun:
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label='Diffusion guidance scale')
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steps_slider = gr.Slider(5, 200, value=75, step=5,
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label='Number of diffusion inference steps')
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run_btn = gr.Button('Run Generation', variant='primary', interactive=False)
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guide_text = gr.Markdown(_USER_GUIDE, visible=True)
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outputs=[elev_output, vis_output, *views]
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).success(fn=partial(update_guide, _GEN_2), outputs=[guide_text], queue=False
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).success(fn=partial(stage2_run, models, device),
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inputs=[tmp_dir, elev_output, scale_slider],
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outputs=[mesh_output]
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).success(fn=partial(update_guide, _DONE), outputs=[guide_text], queue=False)
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outputs=[rerun_idx, *btn_retrys, *views]
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).success(fn=partial(update_guide, _REGEN_1), outputs=[guide_text], queue=False)
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regen_mesh_btn.click(fn=partial(stage2_run, models, device),
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inputs=[tmp_dir, elev_output, scale_slider, rerun_idx],
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outputs=[mesh_output, rerun_idx, regen_view_btn, regen_mesh_btn]
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).success(fn=partial(update_guide, _REGEN_2), outputs=[guide_text], queue=False)
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return (rerun_all, *reset, *outputs)
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def stage2_run(models, device, tmp_dir,
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elev, scale, is_glb=False, rerun_all=[], stage2_steps=50):
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flag_lower_cam = 90-int(elev["label"]) <= 75
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is_rerun = True if rerun_all else False
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model = models['turncam'].half()
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os.chdir(main_dir_path)
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ply_path = os.path.join(tmp_dir, f"meshes_val_bg/lod0/mesh_00215000_gradio_lod0.ply")
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mesh_ext = ".glb" if is_glb else ".obj"
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mesh_path = os.path.join(tmp_dir, f"mesh{mesh_ext}")
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# Read the textured mesh from .ply file
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mesh = trimesh.load_mesh(ply_path)
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axis = [1, 0, 0]
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mesh.vertices[:, 0] = -mesh.vertices[:, 0]
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mesh.faces = np.fliplr(mesh.faces)
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# Export the mesh as .obj file with colors
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if not is_glb:
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mesh.export(mesh_path, file_type='obj', include_color=True)
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else:
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mesh.export(mesh_path, file_type='glb')
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torch.cuda.empty_cache()
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if not is_rerun:
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label='Diffusion guidance scale')
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steps_slider = gr.Slider(5, 200, value=75, step=5,
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label='Number of diffusion inference steps')
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glb_chk = gr.Checkbox(
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False, label='Export the mesh in .glb format')
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run_btn = gr.Button('Run Generation', variant='primary', interactive=False)
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guide_text = gr.Markdown(_USER_GUIDE, visible=True)
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outputs=[elev_output, vis_output, *views]
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).success(fn=partial(update_guide, _GEN_2), outputs=[guide_text], queue=False
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).success(fn=partial(stage2_run, models, device),
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inputs=[tmp_dir, elev_output, scale_slider, glb_chk],
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outputs=[mesh_output]
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).success(fn=partial(update_guide, _DONE), outputs=[guide_text], queue=False)
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outputs=[rerun_idx, *btn_retrys, *views]
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).success(fn=partial(update_guide, _REGEN_1), outputs=[guide_text], queue=False)
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regen_mesh_btn.click(fn=partial(stage2_run, models, device),
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inputs=[tmp_dir, elev_output, scale_slider, glb_chk, rerun_idx],
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outputs=[mesh_output, rerun_idx, regen_view_btn, regen_mesh_btn]
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).success(fn=partial(update_guide, _REGEN_2), outputs=[guide_text], queue=False)
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