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HongFangzhou
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β’
699ffc9
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Parent(s):
8ee45cc
should work now
Browse files- app.py +10 -3
- requirements.txt +6 -6
app.py
CHANGED
@@ -2,9 +2,7 @@ import os
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import sys
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import cv2
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import time
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import tyro
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import json
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import kiui
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import tqdm
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import torch
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import mcubes
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@@ -22,6 +20,13 @@ from huggingface_hub import hf_hub_download
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sys.path.append("3DTopia")
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from ldm.models.diffusion.ddim import DDIMSampler
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from ldm.models.diffusion.plms import PLMSSampler
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from ldm.models.diffusion.dpm_solver import DPMSolverSampler
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@@ -110,6 +115,7 @@ opt.save = GRADIO_SAVE_PATH_MESH
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opt.prompt = ''
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opt.text_dir = True
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opt.front_dir = '+z'
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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gui = GUI(opt)
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###################################### INIT STAGE 2 #########################################
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def marching_cube(b, text, global_info):
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# prepare volumn for marching cube
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res =
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assert 'decode_res' in global_info
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decode_res = global_info['decode_res']
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c_list = torch.linspace(-1.2, 1.2, steps=res)
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@@ -358,6 +364,7 @@ def process_stage2(input_model, input_text, input_dir, iters, output_model, outp
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markdown=f'''
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# 3DTopia
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A two-stage text-to-3D generation model. The first stage uses diffusion model to quickly generate candidates. The second stage refines the assets chosen from the first stage.
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### Usage:
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import sys
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import cv2
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import time
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import json
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import tqdm
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import torch
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import mcubes
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sys.path.append("3DTopia")
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os.system("pip install git+https://github.com/NVlabs/tiny-cuda-nn/#subdirectory=bindings/torch")
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os.system("pip install git+https://github.com/NVlabs/nvdiffrast")
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os.system("pip install git+https://github.com/3DTopia/threefiner")
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import tyro
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import kiui
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from ldm.models.diffusion.ddim import DDIMSampler
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from ldm.models.diffusion.plms import PLMSSampler
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from ldm.models.diffusion.dpm_solver import DPMSolverSampler
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opt.prompt = ''
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opt.text_dir = True
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opt.front_dir = '+z'
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opt.force_cuda_rast = True
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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gui = GUI(opt)
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###################################### INIT STAGE 2 #########################################
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def marching_cube(b, text, global_info):
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# prepare volumn for marching cube
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res = 64
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assert 'decode_res' in global_info
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decode_res = global_info['decode_res']
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c_list = torch.linspace(-1.2, 1.2, steps=res)
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markdown=f'''
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# 3DTopia
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![](https://visitor-badge.laobi.icu/badge?page_id=3DTopia.3DTopia.gradio)
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A two-stage text-to-3D generation model. The first stage uses diffusion model to quickly generate candidates. The second stage refines the assets chosen from the first stage.
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### Usage:
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requirements.txt
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@@ -1,6 +1,7 @@
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torch
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torchvision
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torchaudio
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pytorch-lightning
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numpy
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tqdm
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@@ -55,6 +56,5 @@ vit-pytorch
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wandb
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wcwidth
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zipp
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git+https://github.com/3DTopia/threefiner
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torch==1.13.1+cu117
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torchvision==0.14.1+cu117
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torchaudio==0.13.1
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--extra-index-url https://download.pytorch.org/whl/cu117
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pytorch-lightning
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numpy
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tqdm
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wandb
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wcwidth
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zipp
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kiui
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accelerate
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