Anime Neural Net Graveyard

Used Glow (Kingma & Dhariwal2018) official implementation.

Due to the enormous model size (4.2GB), I had to modify Glow’s settings to get training working reasonably well, after extensive tinkering to figure out what any meant:

{"verbose": true, "restore_path": "logs/model_4.ckpt", "inference": false, "logdir": "./logs", "problem": "asuka",
"category": "", "data_dir": "../glow/data/asuka/", "dal": 2, "fmap": 1, "pmap": 16, "n_train": 20000, "n_test": 1000,
"n_batch_train": 16, "n_batch_test": 50, "n_batch_init": 16, "optimizer": "adamax", "lr": 0.0005, "beta1": 0.9,
"polyak_epochs": 1, "weight_decay": 1.0, "epochs": 1000000, "epochs_warmup": 10, "epochs_full_valid": 3,
"gradient_checkpointing": 1, "image_size": 512, "anchor_size": 128, "width": 512, "depth": 13, "weight_y": 0.0,
"n_bits_x": 8, "n_levels": 7, "n_sample": 16, "epochs_full_sample": 5, "learntop": false, "ycond": false, "seed": 0,
"flow_permutation": 2, "flow_coupling": 1, "n_y": 1, "rnd_crop": false, "local_batch_train": 1, "local_batch_test": 1,
"local_batch_init": 1, "direct_iterator": true, "train_its": 1250, "test_its": 63, "full_test_its": 1000, "n_bins": 256.0, "top_shape": [4, 4, 768]}
...
{"epoch": 5, "n_processed": 100000, "n_images": 6250, "train_time": 14496, "loss": "2.0090", "bits_x": "2.0090", "bits_y": "0.0000", "pred_loss": "1.0000"}

An additional challenge was numerical instability in the reversing of matrices, giving rise to many ‘invertibility’ crashes.

Final sample before I looked up the compute requirements more carefully & gave up on Glow: