content: [data]
  dequant_dist = "none"
  int_dequant_factor = 0
  
  [unimodmlp_params]
  num_layers = 2
  d_token = 4
  n_head = 1
  factor = 32
  bias = true
  dim_t = 1024
  use_mlp = true
  
  [diffusion_params]
  num_timesteps = 50
  scheduler='power_mean'      # 'power_mean', 'power_mean_unified', 'power_mean_per_column'
  cat_scheduler='log_linear'        # 'log_linear', 'log_linear_unified', 'log_linear_per_column'
  noise_dist='uniform_t'       #'uniform_t' or 'log_norm'
  
  [diffusion_params.sampler_params]
  stochastic_sampler = true
  second_order_correction = true
  
  [diffusion_params.edm_params]
  precond = true
  sigma_data = 1.0
  net_conditioning = "sigma"
  
  [diffusion_params.noise_dist_params]
  P_mean = -1.2
  P_std = 1.2
  
  [diffusion_params.noise_schedule_params]
  sigma_min = 0.002
  sigma_max = 80
  rho = 7
  eps_max = 1e-3
  eps_min = 1e-5
  rho_init = 7.0
  rho_offset = 5.0
  k_init=-6.0
  k_offset=1.0
  
  [train.main]
  steps = 8000
  lr = 0.001
  weight_decay = 0
  ema_decay = 0.997
  batch_size = 4096
  check_val_every = 2000
  lr_scheduler = "reduce_lr_on_plateau"
  factor = 0.90           # hyperparam for reduce_lr_on_plateau
  reduce_lr_patience = 50        # hyperparam for reduce_lr_on_plateau
  closs_weight_schedule = "anneal"
  c_lambda = 1.0
  d_lambda = 1.0
  
  [sample]
  batch_size = 10000
  