image_size = 128 # the generated image resolution
train_batch_size = 16
eval_batch_size = 16 # how many images to sample during evaluation
num_epochs = 15000
gradient_accumulation_steps = 1
learning_rate = 1e-5
lr_warmup_steps = 500
save_image_epochs = 50
save_model_epochs = 50
block_out_channels=(128, 128, 256, 256, 512, 512), # the number of output channels for each UNet block
down_block_types=(
"DownBlock2D", # a regular ResNet downsampling block
"DownBlock2D",
"DownBlock2D",
"DownBlock2D",
"AttnDownBlock2D", # a ResNet downsampling block with spatial self-attention
"DownBlock2D",
),
preprocess = transforms.Compose(
[
transforms.Resize((config.image_size, config.image_size)),
transforms.RandomRotation(45),
transforms.RandomHorizontalFlip(),
transforms.RandomVerticalFlip(),
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5]),
]
)
