add source codes

3DTopia/.gitignore

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+__pycache__
+checkpoints
+results
+tmp

3DTopia/LICENSE

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3DTopia/README.md

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+<p align="center">
+    <picture>
+    <img alt="logo" src="assets/3dtopia.jpeg" width="20%">
+    </picture>
+</p>
+<div align="center">
+  <h1>3DTopia</h1>
+  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.
+
+https://github.com/3DTopia/3DTopia/assets/23376858/c9716cf0-6e61-4983-82b2-2e8f579bd46c
+
+</div>
+
+## News
+
+[2024/01/18] We release a text-to-3D model 3DTopia!
+
+## 1. Quick Start
+
+### 1.1 Install Environment for this Repository
+We recommend using Anaconda to manage the environment.
+```bash
+conda env create -f environment.yml
+```
+
+### 1.2 Install Second Stage Refiner
+Please refer to [threefiner](https://github.com/3DTopia/threefiner) to install our second stage mesh refiner. We have tested installing both environments together with Pytorch 1.12.0 and CUDA 11.3.
+
+### 1.3 Download Checkpoints \[Optional\]
+We have implemented automatic checkpoint download for both `gradio_demo.py` and `sample_stage1.py`. If you prefer to download manually, you may download checkpoint `3dtopia_diffusion_state_dict.ckpt` or `model.safetensors` from [huggingface](https://huggingface.co/hongfz16/3DTopia).
+
+### Q&A
+- If you encounter this error in the second stage `ImportError: /lib64/libc.so.6: version 'GLIBC_2.25' not found`, try to install a lower version of pymeshlab by `pip install pymeshlab==0.2`.
+
+## 2. Inference
+
+### 2.1 First Stage
+Run the following command to sample `a robot` as the first stage. Results will be located under the folder `results`.
+```bash
+python -u sample_stage1.py --text "a robot" --samples 1 --sampler ddim --steps 200 --cfg_scale 7.5 --seed 0
+```
+
+Arguments:
+- `--ckpt` specifies checkpoint file path;
+- `--test_folder` controls which subfolder to put all the results;
+- `--seed` will fix random seeds; `--sampler` can be set to `ddim` for DDIM sampling (By default, we use 1000 steps DDPM sampling);
+- `--steps` controls sampling steps only for DDIM;
+- `--samples` controls number of samples;
+- `--text` is the input text;
+- `--no_video` and `--no_mcubes` suppress rendering multi-view videos and marching cubes, which are by-default enabled;
+- `--mcubes_res` controls the resolution of the 3D volumn sampled for marching cubes; One can lower this resolution to save graphics memory;
+- `--render_res` controls the resolution of the rendered video;
+
+### 2.2 Second Stage
+There are two steps as the second stage refinement. Here is a simple example. Please refer to [threefiner](https://github.com/3DTopia/threefiner) for more detailed usage.
+```bash
+# step 1
+threefiner sd --mesh results/default/stage1/a_robot_0_0.ply --prompt "a robot" --text_dir --front_dir='-y' --outdir results/default/stage2/ --save a_robot_0_0_sd.glb
+# step 2
+threefiner if2 --mesh results/default/stage2/a_robot_0_0_sd.glb --prompt "a robot" --outdir results/default/stage2/ --save a_robot_0_0_if2.glb
+```
+The resulting mesh can be found at `results/default/stage2/a_robot_0_0_if2.glb`
+
+## 3. Acknowledgement
+We thank the community for building and open-sourcing the foundation of this work. Specifically, we want to thank [EG3D](https://github.com/NVlabs/eg3d), [Stable Diffusion](https://github.com/CompVis/stable-diffusion) for their codes. We also want to thank [Objaverse](https://objaverse.allenai.org) for the wonderful dataset.

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