1.Environment
Software development environment should be any Python integrated development environment used on an NVIDIA video card.
Environment setting Use commands in env.sh to setup the correct conda environment
2.Usage
For training a single image, use the following command for example:
CUDA_VISIBLE_DEVICES=0 python train_image.py --image-path data/imgs/**/*.jpg --vae-levels 3 --niter 5000 --checkname myimagetest --visualize
Use eval_image.py to generate samples from an "experiment" folder created during training. For example, the following line will generate 100 image samples:
CUDA_VISIBLE_DEVICES=0 python eval_image.py --num-samples 20 --exp-dir run/**/*/experiment_0
results are saved under run/**/*/experiment_0/eval.
In order to extract images, use the extract_images.py files similarly:
CUDA_VISIBLE_DEVICES=0 python extract_images.py --max-samples 20 --exp-dir run/1/myimagetest/experiment_0/eval
results are saved under run/**/*/experiment_0/eval/images.