run `python demo.py`, will load the released text2shape model on hugging face and generate a chair point cloud. (Note: the checkpoint is not released yet, the files loaded in the `demo.py` file is not available at this point)
* run `bash ./script/train_vae.sh $NGPU` (the released checkpoint is trained with `NGPU=4` on A100)
* if want to use comet to log the experiment, add `.comet_api` file under the current folder, write the api key as `{"api_key": "${COMET_API_KEY}"}` in the `.comet_api` file
* this script trains model for single-view-reconstruction or text2shape task
* the idea is that we take the encoder and decoder trained on the data as usual (without conditioning input), and when training the diffusion prior, we feed the clip image embedding as conditioning input: the shape-latent prior model will take the clip embedding through AdaGN layer.
* require the rendered ShapeNet data, you can render yourself or download it from [here](https://github.com/autonomousvision/occupancy_networks#preprocessed-data)
* put the rendered data as `./data/shapenet_render/` or edit the `clip_forge_image` entry in `./datasets/data_path.py`
* the img data will be read under `./datasets/pointflow_datasets.py` with the `render_img_path`, you may need to cutomize this variable depending of the folder structure
* download the test data (Table 1) from [here](https://drive.google.com/file/d/1uEp0o6UpRqfYwvRXQGZ5ZgT1IYBQvUSV/view?usp=share_link), unzip and put it as `./datasets/test_data/`
* please check [here](https://github.com/nv-tlabs/LION/issues/20#issuecomment-1436315100) before using this data
* [all category](https://drive.google.com/file/d/1QXrCbYKjTIAnH1OhZMathwdtQEXG5TjO/view?usp=sharing): 1000 shapes are sampled from the full validation set
* check [here](https://github.com/nv-tlabs/LION/issues/26#issuecomment-1466915318) before using this data
* [mug](https://drive.google.com/file/d/1lvJh2V94Nd7nZPcRqsCwW5oygsHOD3EE/view?usp=share_link) and [bottle](https://drive.google.com/file/d/1MRl4EgW6-4hOrdRq_e2iGh348a0aCH5f/view?usp=share_link)
* download the test data from [here](https://drive.google.com/file/d/1uEp0o6UpRqfYwvRXQGZ5ZgT1IYBQvUSV/view?usp=share_link), unzip and put it as `./datasets/test_data/`