def get_config(): config = get_default_configs() # training training = config.training training.sde = 'vpsde' training.continuous = True training.reduce_mean = True # sampling sampling = config.sampling sampling.method = 'pc' sampling.predictor = 'euler_maruyama' sampling.corrector = 'none' # data data = config.data data.centered = True # model model = config.model model.name = 'ddpm' model.scale_by_sigma = False model.ema_rate = 0.9999 model.normalization = 'GroupNorm' model.nonlinearity = 'swish' model.nf = 128 model.ch_mult = (1, 2, 2, 2) model.num_res_blocks = 2 model.attn_resolutions = (16, ) model.resamp_with_conv = True model.conditional = True return config
def get_config(): config = get_default_configs() # training training = config.training training.sde = 'vesde' training.continuous = False # sampling sampling = config.sampling sampling.method = 'pc' sampling.predictor = 'reverse_diffusion' sampling.corrector = 'langevin' # model model = config.model model.name = 'ddpm' model.scale_by_sigma = True model.ema_rate = 0.999 model.normalization = 'GroupNorm' model.nonlinearity = 'swish' model.nf = 128 model.ch_mult = (1, 2, 2, 2) model.num_res_blocks = 2 model.attn_resolutions = (16, ) model.resamp_with_conv = True model.conditional = True model.conv_size = 3 return config
def get_config(): config = get_default_configs() # training training = config.training training.sde = 'vesde' training.continuous = False # sampling sampling = config.sampling sampling.method = 'pc' sampling.predictor = 'none' sampling.corrector = 'ald' sampling.n_steps_each = 5 sampling.snr = 0.176 # model model = config.model model.name = 'ncsn' model.scale_by_sigma = False model.num_scales = 232 model.ema_rate = 0. 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-3 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 = 'subvpsde' training.continuous = True training.n_iters = 950001 training.reduce_mean = True # sampling sampling = config.sampling sampling.method = 'pc' sampling.predictor = 'euler_maruyama' sampling.corrector = 'none' # data data = config.data data.centered = True # model model = config.model model.name = 'ncsnpp' model.fourier_scale = 16 model.scale_by_sigma = False model.ema_rate = 0.9999 model.normalization = 'GroupNorm' model.nonlinearity = 'swish' model.nf = 128 model.ch_mult = (1, 2, 2, 2) model.num_res_blocks = 8 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 = 'none' model.progressive_input = 'residual' model.progressive_combine = 'sum' model.attention_type = 'ddpm' model.embedding_type = 'positional' model.init_scale = 0.0 model.conv_size = 3 return config
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' # model model = config.model model.name = 'ncsnpp' model.scale_by_sigma = True model.ema_rate = 0.999 model.normalization = 'GroupNorm' model.nonlinearity = 'swish' model.nf = 128 model.ch_mult = (1, 2, 2, 2) model.num_res_blocks = 4 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 = 'none' model.progressive_input = 'residual' model.progressive_combine = 'sum' model.attention_type = 'ddpm' model.init_scale = 0. model.fourier_scale = 16 model.conv_size = 3 return config