Problem when training on my dataset

[soul-M-42]

When training on my own dataset (8.8k 512*512 images), like this:

the process goes well at first but collapsed after a while.
When initializing the output of the model like this:

But as the training continue, the output went strange and lost all the diversity:

Could anyone help me out? I cannot determine where the problem is: the scale of dataset, or the training parameters?

The config I use:

`

training parameters.

parser.add_argument('--lr', type=float, default=0.001) # learning rate.
parser.add_argument('--lr_decay', type=float, default=0.87) # learning rate decay at every resolution transition.
parser.add_argument('--eps_drift', type=float, default=0.001) # coeff for the drift loss.
parser.add_argument('--smoothing', type=float, default=0.997) # smoothing factor for smoothed generator.
parser.add_argument('--nc', type=int, default=3) # number of input channel.
parser.add_argument('--nz', type=int, default=512) # input dimension of noise.
parser.add_argument('--ngf', type=int, default=512) # feature dimension of final layer of generator.
parser.add_argument('--ndf', type=int, default=512) # feature dimension of first layer of discriminator.
parser.add_argument('--TICK', type=int, default=1000) # 1 tick = 1000 images = (1000/batch_size) iter.
parser.add_argument('--max_resl', type=int, default=9) # 10-->1024, 9-->512, 8-->256
parser.add_argument('--trns_tick', type=int, default=200) # transition tick
parser.add_argument('--stab_tick', type=int, default=100) # stabilization tick

network structure.

parser.add_argument('--flag_wn', type=bool, default=True) # use of equalized-learning rate.
parser.add_argument('--flag_bn', type=bool, default=False) # use of batch-normalization. (not recommended)
parser.add_argument('--flag_pixelwise', type=bool, default=True) # use of pixelwise normalization for generator.
parser.add_argument('--flag_gdrop', type=bool, default=True) # use of generalized dropout layer for discriminator.
parser.add_argument('--flag_leaky', type=bool, default=True) # use of leaky relu instead of relu.
parser.add_argument('--flag_tanh', type=bool, default=False) # use of tanh at the end of the generator.
parser.add_argument('--flag_sigmoid', type=bool, default=False) # use of sigmoid at the end of the discriminator.
parser.add_argument('--flag_add_noise', type=bool, default=True) # add noise to the real image(x)
parser.add_argument('--flag_norm_latent', type=bool, default=False) # pixelwise normalization of latent vector (z)
parser.add_argument('--flag_add_drift', type=bool, default=True) # add drift loss

optimizer setting.

parser.add_argument('--optimizer', type=str, default='adam') # optimizer type.
parser.add_argument('--beta1', type=float, default=0.0) # beta1 for adam.
parser.add_argument('--beta2', type=float, default=0.99) # beta2 for adam.
`

[lllllllllllll-llll]

Yes, I have the sample problem.