Spaces:
Runtime error
Runtime error
Working ZeroGPU version
#6
by
cbensimon
HF Staff
- opened
- app.py +10 -25
- freesplatter/webui/runner.py +17 -16
- freesplatter/webui/tab_img_to_3d.py +4 -10
- open3d_zerogpu_fix.py +7 -0
app.py
CHANGED
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@@ -1,6 +1,8 @@
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import os
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if 'OMP_NUM_THREADS' not in os.environ:
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os.environ['OMP_NUM_THREADS'] = '16'
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import torch
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import subprocess
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import gradio as gr
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@@ -11,30 +13,16 @@ from freesplatter.webui.runner import FreeSplatterRunner
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from freesplatter.webui.tab_img_to_3d import create_interface_img_to_3d
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def install_cuda_toolkit():
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CUDA_TOOLKIT_URL = "https://developer.download.nvidia.com/compute/cuda/12.1.0/local_installers/cuda_12.1.0_530.30.02_linux.run"
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CUDA_TOOLKIT_FILE = "/tmp/%s" % os.path.basename(CUDA_TOOLKIT_URL)
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subprocess.call(["wget", "-q", CUDA_TOOLKIT_URL, "-O", CUDA_TOOLKIT_FILE])
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subprocess.call(["chmod", "+x", CUDA_TOOLKIT_FILE])
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subprocess.call([CUDA_TOOLKIT_FILE, "--silent", "--toolkit"])
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os.environ["CUDA_HOME"] = "/usr/local/cuda"
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os.environ["PATH"] = "%s/bin:%s" % (os.environ["CUDA_HOME"], os.environ["PATH"])
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os.environ["LD_LIBRARY_PATH"] = "%s/lib:%s" % (
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os.environ["CUDA_HOME"],
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"" if "LD_LIBRARY_PATH" not in os.environ else os.environ["LD_LIBRARY_PATH"],
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)
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# Fix: arch_list[-1] += '+PTX'; IndexError: list index out of range
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os.environ["TORCH_CUDA_ARCH_LIST"] = "8.0;8.6"
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-
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install_cuda_toolkit()
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-
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torch.set_grad_enabled(False)
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device = torch.device('cuda')
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runner = FreeSplatterRunner(device)
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_HEADER_ = '''
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# FreeSplatter 🤗 Gradio Demo
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\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)
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@@ -82,18 +70,15 @@ with gr.Blocks(analytics_enabled=False, title='FreeSplatter Demo') as demo:
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with gr.Tabs() as sub_tabs_img_to_3d:
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with gr.TabItem('Hunyuan3D Std', id='tab_hunyuan3d_std'):
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_, var_img_to_3d_hunyuan3d_std = create_interface_img_to_3d(
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-
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runner.run_img_to_3d,
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model='Hunyuan3D Std')
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with gr.TabItem('Zero123++ v1.1', id='tab_zero123plus_v11'):
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_, var_img_to_3d_zero123plus_v11 = create_interface_img_to_3d(
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-
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runner.run_img_to_3d,
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model='Zero123++ v1.1')
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with gr.TabItem('Zero123++ v1.2', id='tab_zero123plus_v12'):
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_, var_img_to_3d_zero123plus_v12 = create_interface_img_to_3d(
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runner.run_img_to_3d,
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model='Zero123++ v1.2')
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gr.Markdown(_CITE_)
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import open3d_zerogpu_fix
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import os
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if 'OMP_NUM_THREADS' not in os.environ:
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os.environ['OMP_NUM_THREADS'] = '16'
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import spaces
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import torch
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import subprocess
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import gradio as gr
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from freesplatter.webui.tab_img_to_3d import create_interface_img_to_3d
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torch.set_grad_enabled(False)
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device = torch.device('cuda')
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runner = FreeSplatterRunner(device)
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@spaces.GPU(duration=120)
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def run_img_to_3d(*args):
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yield from runner.run_img_to_3d(*args, cache_dir=gr.utils.get_upload_folder())
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_HEADER_ = '''
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# FreeSplatter 🤗 Gradio Demo
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\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)
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with gr.Tabs() as sub_tabs_img_to_3d:
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with gr.TabItem('Hunyuan3D Std', id='tab_hunyuan3d_std'):
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_, var_img_to_3d_hunyuan3d_std = create_interface_img_to_3d(
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run_img_to_3d,
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model='Hunyuan3D Std')
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with gr.TabItem('Zero123++ v1.1', id='tab_zero123plus_v11'):
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_, var_img_to_3d_zero123plus_v11 = create_interface_img_to_3d(
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run_img_to_3d,
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model='Zero123++ v1.1')
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with gr.TabItem('Zero123++ v1.2', id='tab_zero123plus_v12'):
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_, var_img_to_3d_zero123plus_v12 = create_interface_img_to_3d(
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run_img_to_3d,
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model='Zero123++ v1.2')
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gr.Markdown(_CITE_)
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freesplatter/webui/runner.py
CHANGED
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@@ -1,4 +1,3 @@
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-
import spaces
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import os
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import json
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import uuid
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@@ -157,17 +156,14 @@ class FreeSplatterRunner:
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image,
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do_rembg=True,
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):
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torch.cuda.empty_cache()
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-
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if do_rembg:
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image = remove_background(image, self.rembg)
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return image
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-
@spaces.GPU
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def run_img_to_3d(
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self,
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-
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model='Zero123++ v1.2',
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diffusion_steps=30,
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guidance_scale=4.0,
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@@ -177,7 +173,10 @@ class FreeSplatterRunner:
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mesh_reduction=0.5,
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cache_dir=None,
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):
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-
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self.output_dir = os.path.join(cache_dir, f'output_{uuid.uuid4()}')
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os.makedirs(self.output_dir, exist_ok=True)
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@@ -226,6 +225,10 @@ class FreeSplatterRunner:
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images = images[[0, 2, 4, 5, 3, 1]]
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alphas = alphas[[0, 2, 4, 5, 3, 1]]
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images_vis = v2.functional.to_pil_image(rearrange(images, 'nm c h w -> c h (nm w)'))
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images = v2.functional.resize(images, 512, interpolation=3, antialias=True).clamp(0, 1)
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alphas = v2.functional.resize(alphas, 512, interpolation=0, antialias=True).clamp(0, 1)
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@@ -237,12 +240,12 @@ class FreeSplatterRunner:
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images, alphas = images[view_indices], alphas[view_indices]
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legends = [f'V{i}' if i != 0 else 'Input' for i in view_indices]
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-
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images, alphas, legends=legends, gs_type=gs_type, mesh_reduction=mesh_reduction)
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return images_vis, gs_vis_path, video_path, mesh_fine_path, fig
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@spaces.GPU
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def run_views_to_3d(
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self,
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image_files,
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@@ -251,7 +254,6 @@ class FreeSplatterRunner:
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mesh_reduction=0.5,
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cache_dir=None,
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):
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torch.cuda.empty_cache()
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self.output_dir = os.path.join(cache_dir, f'output_{uuid.uuid4()}')
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os.makedirs(self.output_dir, exist_ok=True)
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gs_type='2DGS',
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mesh_reduction=0.5,
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):
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torch.cuda.empty_cache()
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device = self.device
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freesplatter = self.freesplatter_2dgs if gs_type == '2DGS' else self.freesplatter
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c2ws_pred, focals_pred = freesplatter.estimate_poses(images, gaussians, masks=alphas, use_first_focal=True, pnp_iter=10)
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fig = self.visualize_cameras_object(images, c2ws_pred, focals_pred, legends=legends)
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t2 = time.time()
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# save gaussians
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gs_vis_path = os.path.join(self.output_dir, 'gs_vis.ply')
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save_gaussian(gaussians, gs_vis_path, freesplatter, opacity_threshold=5e-3, pad_2dgs_scale=True)
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print(f'Save gaussian at {gs_vis_path}')
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# render video
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with torch.inference_mode():
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save_video(video_frames, video_path, fps=30)
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print(f'Save video at {video_path}')
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t3 = time.time()
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# extract mesh
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with torch.inference_mode():
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print(f'Generate mesh: {t4-t3:.2f} seconds.')
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print(f'Optimize mesh: {t5-t4:.2f} seconds.')
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-
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def visualize_cameras_object(
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self,
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return fig
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# FreeSplatter-S
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@spaces.GPU
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def run_views_to_scene(
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self,
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image1,
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image2,
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cache_dir=None,
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):
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torch.cuda.empty_cache()
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self.output_dir = os.path.join(cache_dir, f'output_{uuid.uuid4()}')
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os.makedirs(self.output_dir, exist_ok=True)
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images,
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legends=None,
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):
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torch.cuda.empty_cache()
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freesplatter = self.freesplatter_scene
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import os
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import json
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import uuid
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image,
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do_rembg=True,
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):
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if do_rembg:
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image = remove_background(image, self.rembg)
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return image
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def run_img_to_3d(
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self,
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image,
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model='Zero123++ v1.2',
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diffusion_steps=30,
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guidance_scale=4.0,
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mesh_reduction=0.5,
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cache_dir=None,
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):
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image_rgba = self.run_segmentation(image)
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res = [image_rgba]
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yield res + [None] * (6 - len(res))
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self.output_dir = os.path.join(cache_dir, f'output_{uuid.uuid4()}')
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os.makedirs(self.output_dir, exist_ok=True)
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images = images[[0, 2, 4, 5, 3, 1]]
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alphas = alphas[[0, 2, 4, 5, 3, 1]]
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images_vis = v2.functional.to_pil_image(rearrange(images, 'nm c h w -> c h (nm w)'))
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res += [images_vis]
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yield res + [None] * (6 - len(res))
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images = v2.functional.resize(images, 512, interpolation=3, antialias=True).clamp(0, 1)
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alphas = v2.functional.resize(alphas, 512, interpolation=0, antialias=True).clamp(0, 1)
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images, alphas = images[view_indices], alphas[view_indices]
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legends = [f'V{i}' if i != 0 else 'Input' for i in view_indices]
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for item in self.run_freesplatter_object(
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images, alphas, legends=legends, gs_type=gs_type, mesh_reduction=mesh_reduction):
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res += [item]
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yield res + [None] * (6 - len(res))
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def run_views_to_3d(
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self,
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image_files,
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mesh_reduction=0.5,
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cache_dir=None,
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):
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self.output_dir = os.path.join(cache_dir, f'output_{uuid.uuid4()}')
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os.makedirs(self.output_dir, exist_ok=True)
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gs_type='2DGS',
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mesh_reduction=0.5,
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):
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device = self.device
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freesplatter = self.freesplatter_2dgs if gs_type == '2DGS' else self.freesplatter
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c2ws_pred, focals_pred = freesplatter.estimate_poses(images, gaussians, masks=alphas, use_first_focal=True, pnp_iter=10)
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fig = self.visualize_cameras_object(images, c2ws_pred, focals_pred, legends=legends)
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t2 = time.time()
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yield fig
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# save gaussians
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gs_vis_path = os.path.join(self.output_dir, 'gs_vis.ply')
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save_gaussian(gaussians, gs_vis_path, freesplatter, opacity_threshold=5e-3, pad_2dgs_scale=True)
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print(f'Save gaussian at {gs_vis_path}')
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yield gs_vis_path
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# render video
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with torch.inference_mode():
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save_video(video_frames, video_path, fps=30)
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print(f'Save video at {video_path}')
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t3 = time.time()
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yield video_path
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# extract mesh
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with torch.inference_mode():
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print(f'Generate mesh: {t4-t3:.2f} seconds.')
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print(f'Optimize mesh: {t5-t4:.2f} seconds.')
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yield mesh_fine_path
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def visualize_cameras_object(
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self,
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return fig
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# FreeSplatter-S
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def run_views_to_scene(
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self,
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image1,
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image2,
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cache_dir=None,
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):
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self.output_dir = os.path.join(cache_dir, f'output_{uuid.uuid4()}')
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os.makedirs(self.output_dir, exist_ok=True)
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images,
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legends=None,
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):
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freesplatter = self.freesplatter_scene
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freesplatter/webui/tab_img_to_3d.py
CHANGED
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@@ -5,7 +5,7 @@ from .gradio_custommodel3d import CustomModel3D
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from .gradio_customgs import CustomGS
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-
def create_interface_img_to_3d(
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default_views = {
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'Zero123++ v1.1': ['Input', 'V2', 'V3', 'V5'],
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'Zero123++ v1.2': ['V1', 'V2', 'V3', 'V5', 'V6'],
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@@ -137,14 +137,8 @@ def create_interface_img_to_3d(segmentation_api, freesplatter_api, model='Zero12
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)
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var_dict['run_btn'].click(
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fn=
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inputs=var_dict['in_image'],
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outputs=var_dict['fg_image'],
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concurrency_id='default_group',
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api_name='run_segmentation',
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).success(
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fn=partial(freesplatter_api, cache_dir=interface.GRADIO_CACHE),
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inputs=[var_dict['fg_image'],
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var_dict['model'],
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var_dict['diffusion_steps'],
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var_dict['guidance_scale'],
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var_dict['view_indices'],
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var_dict['gs_type'],
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var_dict['mesh_reduction']],
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-
outputs=[var_dict['out_multiview'], var_dict['
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concurrency_id='default_group',
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api_name='run_image_to_3d',
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)
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from .gradio_customgs import CustomGS
|
| 6 |
|
| 7 |
|
| 8 |
+
def create_interface_img_to_3d(freesplatter_api, model='Zero123++ v1.2'):
|
| 9 |
default_views = {
|
| 10 |
'Zero123++ v1.1': ['Input', 'V2', 'V3', 'V5'],
|
| 11 |
'Zero123++ v1.2': ['V1', 'V2', 'V3', 'V5', 'V6'],
|
|
|
|
| 137 |
)
|
| 138 |
|
| 139 |
var_dict['run_btn'].click(
|
| 140 |
+
fn=freesplatter_api,
|
| 141 |
+
inputs=[var_dict['in_image'],
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 142 |
var_dict['model'],
|
| 143 |
var_dict['diffusion_steps'],
|
| 144 |
var_dict['guidance_scale'],
|
|
|
|
| 146 |
var_dict['view_indices'],
|
| 147 |
var_dict['gs_type'],
|
| 148 |
var_dict['mesh_reduction']],
|
| 149 |
+
outputs=[var_dict['fg_image'], var_dict['out_multiview'], var_dict['out_pose'], var_dict['out_gs_vis'], var_dict['out_video'], var_dict['out_mesh']],
|
| 150 |
concurrency_id='default_group',
|
| 151 |
api_name='run_image_to_3d',
|
| 152 |
)
|
open3d_zerogpu_fix.py
ADDED
|
@@ -0,0 +1,7 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import fileinput
|
| 2 |
+
import site
|
| 3 |
+
from pathlib import Path
|
| 4 |
+
|
| 5 |
+
with fileinput.FileInput(f'{site.getsitepackages()[0]}/open3d/__init__.py', inplace=True) as file:
|
| 6 |
+
for line in file:
|
| 7 |
+
print(line.replace('_pybind_cuda.open3d_core_cuda_device_count()', '1'), end='')
|