def get_config():
config = get_default_configs()
# training
training = config.training
training.sde = 'vesde'
training.continuous = True
# sampling
sampling = config.sampling
sampling.method = 'pc'
sampling.predictor = 'reverse_diffusion'
sampling.corrector = 'langevin'
# data
data = config.data
data.dataset = 'CUSTOM'
data.image_size = 128
training.batch_size = 16
data.tfrecords_path = "/content/drive/MyDrive/Training/tf_dataset/tf_dataset-r07.tfrecords"
# model
model = config.model
model.name = 'ncsnpp'
model.sigma_max = 217
model.scale_by_sigma = True
model.ema_rate = 0.999
model.normalization = 'GroupNorm'
model.nonlinearity = 'swish'
model.nf = 128
model.ch_mult = (1, 1, 2, 2, 2, 2, 2)
model.num_res_blocks = 2
model.attn_resolutions = (16, )
model.resamp_with_conv = True
model.conditional = True
model.fir = True
model.fir_kernel = [1, 3, 3, 1]
model.skip_rescale = True
model.resblock_type = 'biggan'
model.progressive = 'output_skip'
model.progressive_input = 'input_skip'
model.progressive_combine = 'sum'
model.attention_type = 'ddpm'
model.init_scale = 0.
model.fourier_scale = 16
model.conv_size = 3
return config
def get_config():
config = get_default_configs()
# training
training = config.training
training.sde = 'vpsde'
training.continuous = False
training.reduce_mean = True
# sampling
sampling = config.sampling
sampling.method = 'pc'
sampling.predictor = 'ancestral_sampling'
sampling.corrector = 'none'
# data
data = config.data
data.dataset = 'CelebAHQ'
data.centered = True
data.tfrecords_path = '/atlas/u/yangsong/celeba_hq/-r10.tfrecords'
data.image_size = 256
# model
model = config.model
model.name = 'ddpm'
model.scale_by_sigma = False
model.num_scales = 1000
model.ema_rate = 0.9999
model.normalization = 'GroupNorm'
model.nonlinearity = 'swish'
model.nf = 128
model.ch_mult = (1, 1, 2, 2, 4, 4)
model.num_res_blocks = 2
model.attn_resolutions = (16, )
model.resamp_with_conv = True
model.conditional = True
# optim
optim = config.optim
optim.lr = 2e-5
return config
def get_config():
config = get_default_configs()
# training
training = config.training
training.batch_size = 128
training.sde = 'vesde'
training.continuouse = False
# sampling
sampling = config.sampling
sampling.method = 'pc'
sampling.predictor = 'none'
sampling.corrector = 'ald'
sampling.n_steps_each = 3
sampling.snr = 0.095
# data
data = config.data
data.category = 'bedroom'
data.image_size = 128
# model
model = config.model
model.name = 'ncsnv2_128'
model.scale_by_sigma = True
model.sigma_max = 190
model.num_scales = 1086
model.ema_rate = 0.9999
model.sigma_min = 0.01
model.normalization = 'InstanceNorm++'
model.nonlinearity = 'elu'
model.nf = 128
model.interpolation = 'bilinear'
# optim
optim = config.optim
optim.weight_decay = 0
optim.optimizer = 'Adam'
optim.lr = 1e-4
optim.beta1 = 0.9
optim.amsgrad = False
optim.eps = 1e-8
optim.warmup = 0
optim.grad_clip = -1
return config
def get_config():
config = get_default_configs()
# training
training = config.training
training.sde = 'vpsde'
training.continuous = False
training.reduce_mean = True
# sampling
sampling = config.sampling
sampling.method = 'pc'
sampling.predictor = 'ancestral_sampling'
sampling.corrector = 'none'
# data
data = config.data
data.category = 'church_outdoor'
data.centered = True
# model
model = config.model
model.name = 'ddpm'
model.scale_by_sigma = False
model.num_scales = 1000
model.ema_rate = 0.9999
model.normalization = 'GroupNorm'
model.nonlinearity = 'swish'
model.nf = 128
model.ch_mult = (1, 1, 2, 2, 4, 4)
model.num_res_blocks = 2
model.attn_resolutions = (16, )
model.resamp_with_conv = True
model.conditional = True
# optim
optim = config.optim
optim.lr = 2e-5
return config
