Increase Training Iterations in StyleGAN - machine-learning

Question

I’m a complete noob trying to learn/play with StyleGAN.
I’m using StyleGAN to train a model with an image set that I built, but the output image quality is poor. The training stops after 10 ticks.
How can I change the default parameters in the code to continue training past 10 ticks?
Do I need to change the kimg or fid50k settings? Are the settings in this block of the train.py file? or the metrics file?
{ 'D': {'func_name': 'training.networks_stylegan.D_basic'},
'D_loss': {'func_name': 'training.loss.D_logistic_simplegp', 'r1_gamma': 10.0},
'D_opt': {'beta1': 0.0, 'beta2': 0.99, 'epsilon': 1e-08},
'G': {'func_name': 'training.networks_stylegan.G_style'},
'G_loss': {'func_name': 'training.loss.G_logistic_nonsaturating'},
'G_opt': {'beta1': 0.0, 'beta2': 0.99, 'epsilon': 1e-08},
'dataset': {'resolution': 64, 'tfrecord_dir': 'fab_dataset'},
'desc': 'sgan-fab_dataset-1gpu',
'grid': {'layout': 'random', 'size': '4k'},
'metrics': [{'func_name': 'metrics.frechet_inception_distance.FID', 'minibatch_per_gpu': 8, 'name': 'fid50k', 'num_images': 50000}],
'sched': { 'D_lrate_dict': {128: 0.0015, 256: 0.002, 512: 0.003, 1024: 0.003},
'G_lrate_dict': {128: 0.0015, 256: 0.002, 512: 0.003, 1024: 0.003},
'lod_initial_resolution': 8,
'minibatch_base': 4,
'minibatch_dict': {4: 128, 8: 128, 16: 128, 32: 64, 64: 32, 128: 16, 256: 8, 512: 4}},
'tf_config': {'rnd.np_random_seed': 1000},
'train': {'mirror_augment': False, 'run_func_name': 'training.training_loop.training_loop', 'total_kimg': 25000}}
Training output log:
tick 1 kimg 140.3 lod 4.00 minibatch 128 time 8m 38s sec/tick 461.9 sec/kimg 3.29 maintenance 56.1 gpumem 3.0
network-snapshot-000140 time 13m 11s fid50k 353.7462
tick 2 kimg 280.6 lod 4.00 minibatch 128 time 29m 42s sec/tick 453.7 sec/kimg 3.23 maintenance 809.9 gpumem 3.4
tick 3 kimg 420.9 lod 4.00 minibatch 128 time 37m 16s sec/tick 453.8 sec/kimg 3.23 maintenance 0.9 gpumem 3.4
tick 4 kimg 561.2 lod 4.00 minibatch 128 time 44m 52s sec/tick 454.4 sec/kimg 3.24 maintenance 0.9 gpumem 3.4
tick 5 kimg 681.5 lod 3.87 minibatch 128 time 1h 01m 06s sec/tick 973.4 sec/kimg 8.09 maintenance 0.9 gpumem 4.5
tick 6 kimg 801.8 lod 3.66 minibatch 128 time 1h 21m 43s sec/tick 1235.8 sec/kimg 10.27 maintenance 1.6 gpumem 4.5
tick 7 kimg 922.1 lod 3.46 minibatch 128 time 1h 42m 20s sec/tick 1235.3 sec/kimg 10.27 maintenance 1.2 gpumem 4.5
tick 8 kimg 1042.4 lod 3.26 minibatch 128 time 2h 02m 57s sec/tick 1235.9 sec/kimg 10.27 maintenance 1.2 gpumem 4.5
tick 9 kimg 1162.8 lod 3.06 minibatch 128 time 2h 23m 34s sec/tick 1236.4 sec/kimg 10.28 maintenance 1.2 gpumem 4.5
tick 10 kimg 1283.1 lod 3.00 minibatch 128 time 2h 43m 58s sec/tick 1222.6 sec/kimg 10.16 maintenance 1.2 gpumem 4.5
network-snapshot-001283 time 14m 20s fid50k 349.5387
I can post the complete train.py file if required but it is standard version available on git.
https://github.com/NVlabs/stylegan/blob/master/train.py