To install and activate the environment, run the following command:
conda env create -f environment.yml -p env
conda activate ./env
This repo is built on top of StyleGAN2-ADA, so make sure that it runs on your system.
Data should be stored in zip archives, the exact structure is not important, the script will be used all the found images.
For FFHQ and Cats, we use the same data processing as GRAM.
Put your datasets into data/ directory.
If you want to train with camera angles enabled, then create a dataset.json with camera_angles dict of "<FILE_NAME>": [yaw, pitch, roll] key/values.
Also, use model.discriminator.camera_cond=true model.discriminator.camera_cond_drop_p=0.5 command line arguments (or simply override them in the config).
If you want to train on a custom dataset, then create its config under configs/dataset folder.
Data links:
To launch training, run:
python src/infra/launch.py hydra.run.dir=. exp_suffix=min0.125-anneal10k-gamma0.05-dblocks3-cameracond-drop0.5-cin dataset=ffhq_posed dataset.resolution=512 training=patch_beta training.patch.min_scale_trg=0.25 training.patch.anneal_kimg=10000 model=eg3d training.metrics=fid2k_full env=local training.patch.resolution=64 model.discriminator.num_additional_start_blocks=3 training.kimg=100000 training.gamma=0.05 model.generator.tri_plane.res=512
To continue training, launch:
python src/infra/launch.py hydra.run.dir=. experiment_dir=<PATH_TO_EXPERIMENT> training.resume=latest
At train time, we compute FID only on 2,048 fake images (vs all real images), since generating 50,000 images takes too long. To compute FID for 50k fake images, run:
python src/scripts/calc_metrics.py hydra.run.dir=. ckpt.networks_dir=<CKPT_DIR> script.data=<PATH_TO_DATA> script.mirror=true script.gpus=4 script.metrics=fid50k_full img_resolution=256 ckpt.selection_metric=fid2k_full ckpt.reload_code=true script=calc_metrics
We provide a lot of visualizations types, with the entry point being `configs/scripts/inference.yaml'. For example, to create the main front grid, run:
python src/scripts/inference.py hydra.run.dir=. ckpt.networks_dir=<CKPT_DIR> vis=front_grid camera=points output_dir=<OUTPUT_DIR> num_seeds=16 truncation_psi=0.7
To create visualization videos, run:
python src/scripts/inference.py hydra.run.dir=. ckpt.networks_dir=<CKPT_DIR> output_dir=<OUTPUT_DIR> vis=video camera=front_circle camera.num_frames=64 vis.fps=30 num_seeds=16
To render the megascans, obtain the necessary models from the website, convert them into GLTF, create a enb.blend Blender environment, and then run:
blender --python render_dataset.py env.blend --background
The rendering config is located in render_dataset.py.