Update aydao config comments

train.py

@@ -213,16 +213,24 @@ def setup_training_options(
     args.G_args.conv_clamp = args.D_args.conv_clamp = 256 # clamp activations to avoid float16 overflow
 
     if cfg == 'aydao':
+        # disable path length and style mixing regularization
         args.loss_args.pl_weight = 0
         args.G_args.style_mixing_prob = None
+
+        # double generator capacity
         args.G_args.fmap_base = 32 << 10
         args.G_args.fmap_max = 1024
+
+        # enable top k training
         args.loss_args.G_top_k = True
+        # args.loss_args.G_top_k_gamma = 0.99 # takes ~70% of full training from scratch to decay to 0.5
         # args.loss_args.G_top_k_gamma = 0.9862 # takes 12500 kimg to decay to 0.5 (~1/2 of total_kimg when training from scratch)
         args.loss_args.G_top_k_gamma = 0.9726 # takes 6250 kimg to decay to 0.5 (~1/4 of total_kimg when training from scratch)
         args.loss_args.G_top_k_frac = 0.5
-        args.minibatch_gpu = 2 # probably will need to set this pretty low with such a large G, higher values work better for top-k training though
-        # args.G_args.num_fp16_res = 6 # making more layers fp16 can help as well
+
+        # reduce in-memory size, you need a BIG GPU for this model
+        args.minibatch_gpu = 4 # probably will need to set this pretty low with such a large G, higher values work better for top-k training though
+        args.G_args.num_fp16_res = 6 # making more layers fp16 can help as well
 
     if cfg == 'cifar' or cfg.split('-')[-1] == 'complex':
         args.loss_args.pl_weight = 0 # disable path length regularization