the loss became nan after a few train steps about gigagan-pytorch HOT 2 CLOSED

this is my train code
`import torch

from gigagan_pytorch import (
GigaGAN,
ImageDataset, TextImageDataset
)
from safetensors import safe_open

gan = GigaGAN(
generator = dict(
dim_capacity = 8,
style_network = dict(
dim = 64,
depth = 4,
dim_text_latent = 64
),
image_size = 512,
dim_max = 512,
num_skip_layers_excite = 4,
unconditional = False,
text_encoder = dict(
dim = 64,
depth = 4
)
),
discriminator = dict(
dim_capacity = 16,
dim_max = 512,
image_size = 512,
num_skip_layers_excite = 4,
unconditional = False,
text_encoder = dict(
dim = 64,
depth = 4
)
),
amp = True,
save_and_sample_every = 500,
model_folder = './gigagan-models/',
results_folder = './gigagan-results/',
).cuda()

dataset = TextImageDataset(
folder = '***',
image_size = 512
)

dataloader = dataset.get_dataloader(batch_size = 1)

gan.set_dataloader(dataloader)

gan(
steps = 10000,
grad_accum_every = 8
)
gan.save('gigagan-models/***/final.ckpt')
print("model saved!")
`

from gigagan-pytorch.

Did you fix it?

from gigagan-pytorch.