A suitable conda environment named ealdm can be created and activated with:

conda env create -f environment.yaml
conda activate ealdm

Logs and checkpoints for trained models are saved to logs/<START_DATE_AND_TIME>_<config_spec>.

Configs for training are provided at configs/autoencoder. Training can be started by running

CUDA_VISIBLE_DEVICES=<GPU_ID> python main.py --base configs/autoencoder/<config_spec>.yaml -t --gpus 0,    

where config_spec is one of {autoencoder_kl_8x8x64(f=32, d=64), autoencoder_kl_16x16x16(f=16, d=16), autoencoder_kl_32x32x4(f=8, d=4), autoencoder_kl_64x64x3(f=4, d=3)}.

For training VQ-regularized models, see the taming-transformers repository.

In configs/latent-diffusion/ we provide configs. Training can be started by running for conditioned model:

CUDA_VISIBLE_DEVICES=<GPU_ID> python main.py --base configs/latent-diffusion/stdiff_cin-ldm-vq-f8.yaml -t --gpus 0,

for unconditioned model:

CUDA_VISIBLE_DEVICES=<GPU_ID> python main.py --base configs/latent-diffusion/uncond_cin-ldm-vq-f8.yaml -t --gpus 0,

Running the following script downloads and extracts all available pretrained autoencoding models.

bash scripts/download_first_stages.sh

The first stage models can then be found in models/first_stage_models/<model_spec>